The latest generation of emoticons which are called as emojis that is largely being used in mobile
communications as well as in social media. In past few years, more than ten billion emojis were used on Twitter.
Emojis which are known as the Unicode graphic symbols, which are basically used as shorthand to express the
concepts and ideas of the people. For smaller number of well-known emoticons, their meanings or sentiments
are well known but there are thousands of emojis so extracting their sentiments is difficult. The Emoji Sentiment
Ranking method which is used to evaluate a sentiment mapping of emojis by using sentiment polarity such as
negative, neutral, or positive. The sentimental classification of tweets with and without emoticons are very much
different.Finally, the method also gives representation of sentiments and a better visualization in the form of a
sentimental Bar.
IRJET- A Survey on Graph based Approaches in Sentiment Analysis
This document summarizes research on graph-based approaches for sentiment analysis. It discusses different graph-based techniques proposed in previous studies, including using graphs to model relationships between tweets containing the same hashtag, between n-grams in documents, and between users, tweets, and features on Twitter. It also categorizes related works based on the proposed method, approach used, dataset, and limitations. The document concludes that graph-based approaches can provide higher accuracy for sentiment classification than other methods by capturing semantic relationships.
RULE-BASED SENTIMENT ANALYSIS OF UKRAINIAN REVIEWS
This document summarizes a research paper on rule-based sentiment analysis of Ukrainian reviews. It presents the general architecture of a sentiment analysis system implemented for Ukrainian reviews using a rule-based approach. Key aspects include using a sentiment dictionary generated from an annotated Ukrainian review corpus, identifying sentiments and emotions of individual words, and defining rules to compute sentiments at the clause level based on word order and syntactic structure. The goal is to analyze sentiment at the clause level for a more nuanced understanding of opinions expressed in reviews.
Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Re...
Any E-Commerce website gets bad reputation if they
sell a product which has bad review, the user blames the eCommerce website rather than manufacturers most of the
times. In some review sites some great audits are included by
the item organization individuals itself so as to make so as to
deliver false positive item reviews. To eliminate these type of
fake product review, we will create a system that finds out the
fake reviews and eliminates all the fake reviews by using
machine learning. We also remove the reviews that are flood
by a marketing agency in order to boost up the ratings of a
particular product .Finally Sentiment analysis is done for the
genuine reviews to classify them into positive and negative.
We will use Bag-of-words to label individual words
according to their sentiment.
IRJET- Review on Mood Detection using Image Processing and Chatbot using Arti...IRJET Journal
This document discusses two approaches for mood detection: 1) Using image processing to analyze facial expressions in images to detect mood. Features like eyes, nose, brows and cheeks are analyzed over time to identify expressions that correlate with moods. 2) Using artificial intelligence to build a chatbot that detects user mood based on text responses during a conversation. The chatbot aims to have natural conversations without revealing it is not human. Both approaches aim to provide relevant content like jokes or music to improve the user's mood based on what is detected.
This document summarizes a survey of opinion mining and sentiment analysis techniques. It discusses how opinion mining uses natural language processing and machine learning to analyze sentiment in text sources like blogs, reviews and social media. It outlines several key tasks in opinion mining including sentiment classification at the document, sentence and feature levels. Supervised, unsupervised and semi-supervised machine learning algorithms are commonly used for sentiment classification tasks. Naive Bayes classification and text classification algorithms are also discussed.
The document presents a study that analyzes sentiment on Twitter using various classification algorithms. It compares the performance of Naive Bayes, Bayes Net, Discriminative Multinomial Naive Bayes, Sequential Minimal Optimization, Hyperpipes, and Random Forest algorithms on a Twitter sentiment dataset. The study finds that Discriminative Multinomial Naive Bayes and Sequential Minimal Optimization algorithms have the best performance with overall F-scores of 0.769 and 0.75, respectively. The study aims to determine the most accurate and efficient algorithms for Twitter sentiment classification.
IRJET- A Survey on Graph based Approaches in Sentiment AnalysisIRJET Journal
This document summarizes research on graph-based approaches for sentiment analysis. It discusses different graph-based techniques proposed in previous studies, including using graphs to model relationships between tweets containing the same hashtag, between n-grams in documents, and between users, tweets, and features on Twitter. It also categorizes related works based on the proposed method, approach used, dataset, and limitations. The document concludes that graph-based approaches can provide higher accuracy for sentiment classification than other methods by capturing semantic relationships.
RULE-BASED SENTIMENT ANALYSIS OF UKRAINIAN REVIEWSijaia
This document summarizes a research paper on rule-based sentiment analysis of Ukrainian reviews. It presents the general architecture of a sentiment analysis system implemented for Ukrainian reviews using a rule-based approach. Key aspects include using a sentiment dictionary generated from an annotated Ukrainian review corpus, identifying sentiments and emotions of individual words, and defining rules to compute sentiments at the clause level based on word order and syntactic structure. The goal is to analyze sentiment at the clause level for a more nuanced understanding of opinions expressed in reviews.
Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Re...Dr. Amarjeet Singh
Any E-Commerce website gets bad reputation if they
sell a product which has bad review, the user blames the eCommerce website rather than manufacturers most of the
times. In some review sites some great audits are included by
the item organization individuals itself so as to make so as to
deliver false positive item reviews. To eliminate these type of
fake product review, we will create a system that finds out the
fake reviews and eliminates all the fake reviews by using
machine learning. We also remove the reviews that are flood
by a marketing agency in order to boost up the ratings of a
particular product .Finally Sentiment analysis is done for the
genuine reviews to classify them into positive and negative.
We will use Bag-of-words to label individual words
according to their sentiment.
The document describes a proposed Emotional Cognitive Conversational Agent Architecture (ECCAA) for building chatbots. ECCAA is a 7-layer architecture based on cognitive theories like the Society of Minds approach. Each layer is connected to a database storing conversations and handles different levels of intelligence, from reflexive responses to meta-cognition. The layers were implemented in a chatbot prototype to analyze responses. Results showed ECCAA can achieve responses closer to human conversations compared to general conversational agents.
IRJET- Real Time Sentiment Analysis of Political Twitter Data using Machi...IRJET Journal
This document summarizes a research paper that analyzed sentiments of political tweets related to the Ayodhya issue in India using machine learning. It collected tweets using keywords and preprocessed them by removing URLs, usernames, stop words, and irrelevant data. It then extracted sentiment-bearing words as features. It classified the polarity of each tweet as positive, negative, or neutral using the Vader sentiment analysis tool and calculated overall sentiment scores. It aimed to analyze public opinion on the Ayodhya issue expressed on Twitter.
This document discusses issues in sentiment analysis and emotion extraction from text. It provides an overview of different techniques used for emotion extraction like text mining, empirical studies, emotion extraction engines, and vector space models. It then analyzes the issues with each technique, such as only identifying the subject but not sentiment, inability to determine intensity, and difficulties with contradictory or symbolic text. The document concludes that combining the study of multiple techniques and parameters could help develop a more accurate system for sentiment analysis that is closer to realistic human emotion extraction from text.
Emoji’s sentiment score estimation using convolutional neural network with mu...IJECEIAES
Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi- scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively.
Emotion detection on social media status in Myanmar language IJECEIAES
Many social media emerged and provided services during these years. Most people, especially in Myanmar, use them to express their emotions or moods, learn subjects, sell products, read up-to-date news, and communicate with each other. Emotion detection on social users makes critical tasks in the opinion mining and sentiment analysis. This paper presents the emotion detection system on social media (Facebook) user status or post written in Myanmar (Burmese) language. Before the emotion detection process, the user posts are pre-processed under segmentation, stemming, part-of-speech (POS) tagging, and stop word removal. The system then uses our preconstructed Myanmar word-emotion Lexicon, M-Lexicon, to extract the emotion words from the segmented POS post. The system provides six types of emotion such as surprise, disgust, fear, anger, sadness, and happiness. The system applies naïve Bayes (NB) emotion classifier to examine the emotion in the case of more than two words with different emotion values are extracted. The classifiers also classify the emotion of the users on their posts. The experiment shows that the system can detect 85% accuracy in NB based emotion detection while 86% in recurrent neural network (RNN).
Text to Emotion Extraction Using Supervised Machine Learning TechniquesTELKOMNIKA JOURNAL
Proliferation of internet and social media has greatly increased the popularity of text
communication. People convey their sentiment and emotion through text which promotes lively
communication. Consequently, a tremendous amount of emotional text is generated on different social
media and blogs in every moment. This has raised the necessity of automated tool for emotion mining from
text. There are various rule based approaches of emotion extraction form text based on emotion intensity
lexicon. However, creating emotion intensity lexicon is a time consuming and tedious process. Moreover,
there is no hard and fast rule for assigning emotion intensity to words. To solve these difficulties, we
propose a machine learning based approach of emotion extraction from text which relies on annotated
example rather emotion intensity lexicon. We investigated Multinomial Naïve Bayesian (MNB) Classifier,
Artificial Neural Network (ANN) and Support Vector Machine (SVM) for mining emotion from text. In our
setup, SVM outperformed other classifiers with promising accuracy.
This document discusses issues in sentiment analysis and emotion extraction from text. It provides an overview of natural language processing and its applications. The document then discusses the need for sentiment analysis in areas like artificial intelligence. It proceeds to compare different techniques for emotion extraction from text, including text mining, empirical studies, emotion extraction engines, vector space models, and emotion markup languages. For each technique, it outlines the general approach and provides examples or tables to illustrate how emotions can be identified from text. However, it notes that current applications have not achieved 100% accuracy in realistic sentiment analysis.
This document summarizes research on sentiment analysis of English and Tamil tweets using path length similarity-based word sense disambiguation. It discusses translating Tamil tweets to English, finding semantic similarity using path length in a lexical database, and classifying sentiments using support vector machines. The paper also reviews related work on multilingual sentiment analysis and adaptation to new topics, and proposes a framework to determine sentiment polarity of bilingual tweets.
THE ANALYSIS FOR CUSTOMER REVIEWS THROUGH TWEETS, BASED ON DEEP LEARNINGIRJET Journal
The document describes a study that analyzes customer reviews on Twitter about hotels using deep learning techniques. Twitter data is collected using Python's Tweepy library and preprocessed by removing noise like retweets, URLs and hashtags. The data is then split using scikit-learn into training, validation and testing sets. Tokenization is performed to convert text to vectors and sentiment analysis is done using techniques like Bi-Sense Emoji Embedding (BSEE), Random Forest (RF) and Support Vector Machine (SVM). The performance of BSEE is compared based on accuracy, recall, precision and time taken and is found to provide better results.
ADAPTIVE VOCABULARY CONSTRUCTION FOR FRUSTRATION INTENSITY MODELLING IN CUSTO...IJCSITJournal2
This paper examines emotion intensity prediction in dialogs between clients and customer support
representatives occurring on Twitter. We focus on a single emotion type, namely, frustration, modelling the
user's level of frustration while attempting to predict frustration intensity on the current and next turn,
based on the text of turns coming from both dialog participants. A subset of the Kaggle Customer Support
on Twitter dataset was used as the modelling data, annotated with per-turn frustration intensity ratings.
We propose to represent dialog turns by binary encoded bags of automatically selected keywords to be
subsequently used in a machine learning classifier.
Word and Sentence Level Emotion Analyzation in Telugu Blog and NewsIJCSEA Journal
Emotion analysis, a recent sub discipline at the crossroads of information retrieval and computational linguistics is becoming increasingly important from application viewpoints of affective computing.Emotion is crucial to identify as it is not open to any objective observation or verification. In this paper, emotion analysis on blog texts has been carried out for a less privileged language, Telugu and the same system has been applied on the English SemEval 2007 affect sensing corpus containing only news headlines. A set of six emotion tags, namely, happy ( ), sad ( ), anger ( ), fear ( ), surprise ( )and disgust ( ), have been selected towards this emotion detection task for reliable and semi-automatic annotation of blog and news data. Conditional Random Field (CRF) based classifier has been applied for recognizing six basic emotion tags for different words of a sentence. The classifier accuracy has been improved by arranging an equal distribution of emotional tags and non-emotional tag. A score based technique has been adopted to calculate and assign tag weights to each of the six emotion tags. A sense based scoring strategy has been applied to identify sentence level emotion scores for the six emotion tags based on the acquired word level emotion tags. Sentence level emotion tagging has been
carried out based on the maximum obtained sentence level emotion scores. Evaluation has been conducted for each emotion class separately on 200 test sentences from each of the Telugu blog and English news data. The system has resulted accuracies of 69.82% and 71.06% for happy, 70.24% and 66.42% for sad, 65.73% and 64.27% for anger, 76.01% and 69.90% for disgust, 72.19% and 73.59% for fear and 70.54% and 66.64% for surprise emotion classes on blog and news test data respectively.
This document discusses sentiment analysis on tweets regarding electronic products. It presents the following:
1) It creates a dataset of 1000 tweets (600 positive, 400 negative) on electronic products collected from Twitter using an API.
2) It proposes a 3-phase approach: pre-processing tweets, creating a feature vector using relevant features like hashtags, emoticons, keywords, and classifying tweets using algorithms like Naive Bayes, SVM, and an ensemble method.
3) It evaluates the different classifiers and finds they achieve similar accuracy of around 90%, with Naive Bayes being slightly lower, demonstrating the quality of the selected feature vector for the product domain.
The document describes a project to develop an emoji generation system using deep learning. The system aims to classify human facial expressions into seven emotion categories (e.g. happy, sad, angry) using a convolutional neural network trained on the Fer2013 dataset. It will then map the classified emotions to corresponding emoji icons. The system is intended to enhance online communication by allowing users to express emotions through customized emojis generated from their facial expressions.
This document discusses using machine learning and deep learning techniques for sentiment analysis on social media data. It proposes a system to classify tweets as having positive, negative or neutral sentiment. The system involves data acquisition from Twitter, preprocessing tweets, extracting features, and applying machine learning classifiers like SVR, Random Forest and Decision Tree. It aims to analyze public sentiment on topics and help organizations understand people's views.
Emotional Multi-Agents System for Peer to peer E-LearningKarlos Svoboda
The document describes EMASPEL, an emotional multi-agent system for peer-to-peer e-learning. The system uses multiple agent types, including interface agents, emotional agents, curriculum agents, tutor agents, and emotional embodied conversational agents. Emotional agents analyze learners' facial expressions to recognize emotions, while embodied conversational agents express emotions through facial animations. Together this allows the system to personalize instruction based on learners' cognitive and emotional states.
The document describes a research project on sentiment analysis of tweets. It involves collecting twitter data, preprocessing the data by removing stopwords and replacing emoticons/sentiment words with tags. Features are then extracted and normalized, followed by feature reduction. The data is clustered into positive and negative classes using K-means clustering and Differential Evolution algorithm, and their accuracies are compared, with Differential Evolution found to perform better. Future work proposed includes applying additional clustering techniques and comparing with supervised learning methods.
Review on Opinion Mining for Fully Fledged Systemijeei-iaes
Humans communication is generally under the control of emotions and full of opinions. Emotions an d their opinions plays an important role in thinking process of mind, influences the human actions too. Sentiment analysis is one of the ways to explore user’s opinion made on any social media and networking site for various commercial applications in number of fields. This paper takes into account the basis requirements of opinion mining to explore the present techniques used to develop a fully fledged system. Is highlights the opportunities or deployment and research of such systems. The available tools used for building such applications have even presented with their merits and limitations.
The document provides an overview of sentiment analysis and summarizes the current approaches used. It discusses how machine learning classifiers like Naive Bayes can be used for sentiment classification of texts, treating it as a two-class text classification problem. It also mentions the use of natural language processing techniques. The current system discussed will use machine learning and NLP for sentiment analysis of tweets, training classifiers on labeled tweet data to classify the polarity of new tweets.
A review on sentiment analysis and emotion detection.pptxvoicemail1
This document provides an overview of sentiment analysis and emotion detection from text. It discusses how social media generates massive amounts of textual data that can be analyzed using these techniques. The document outlines several key topics:
- The levels of sentiment analysis including sentence, document and aspect levels.
- Popular emotion models like dimensional and categorical models.
- The basic steps involved in sentiment/emotion detection including preprocessing, feature extraction, and classification.
- Challenges in the field like dealing with context, slang, and ambiguity.
It provides examples of techniques like lexicon-based, machine learning-based and deep learning-based approaches.
IRJET- Dynamic Emotion Recognition and Emoji GenerationIRJET Journal
This document discusses a proposed system called Dynamic Emotion Recognition and Emoji Generation (DEmoji) that aims to improve upon existing static emoji generation systems. DEmoji will use facial tracking and expression recognition to dynamically generate emojis in real-time based on a person's facial expressions. It will also allow for generation of personalized emojis and sharing across different devices. The proposed system aims to provide a more innovative tool for digitally expressing emotions compared to current systems.
Literature Review On: ”Speech Emotion Recognition Using Deep Neural Network”IRJET Journal
The document discusses speech emotion recognition using deep neural networks. It first provides an overview of SER and the challenges in the field. It then reviews 20 research papers on the topic, finding that most use deep neural network techniques like CNNs and DNNs for model building. The papers evaluated various datasets and algorithms, with accuracy ranging from 84% to 90%. Overall limitations identified included the need for more data, handling of multiple simultaneous emotions, and improving cross-corpus performance. The literature review contributes to knowledge in using machine learning for SER.
A Subjective Feature Extraction For Sentiment Analysis In Malayalam LanguageJeff Nelson
The document discusses sentiment analysis of Malayalam film reviews using machine learning techniques. It proposes using Conditional Random Fields combined with rule-based approaches for sentiment analysis at the sentence and document level in Malayalam. The system is trained on a manually tagged corpus of over 30,000 tokens and tested on film reviews to determine the overall polarity (positive, negative, neutral) and rating of individual categories like film, direction, acting etc. The system achieved an accuracy of 82% in identifying sentiment and ratings.
Similar to Extraction of Emoticons with Sentimental Bar (20)
Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This document discusses the importance of leadership in leading an organization towards improvement and development. It states that leadership is responsible for providing a clear vision and strategy to successfully achieve that vision. Effective leadership can impact the success of an organization by controlling its direction and motivating employees. Leadership is different from traditional management in that it guides employees towards organizational goals through open communication and motivation, rather than simply directing work. The paper concludes that only leadership can lead an organization to change according to its evolving environment, while management may simply follow old rules. Leadership is key to adapting to new market needs and trends.
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
The document summarizes research on a nano composite polymer gel electrolyte containing SiO2 nanoparticles. Key points:
1. Polyvinylidene fluoride-co-hexafluoropropylene polymer was used as the base polymer mixed with propylene carbonate, magnesium perchlorate, and SiO2 nanoparticles to synthesize the nano composite polymer gel electrolyte.
2. The electrolyte was characterized using XRD, SEM, and FTIR which confirmed the homogeneous dispersion of SiO2 nanoparticles and increased amorphous nature of the electrolyte, enhancing its ion conductivity.
3. XRD showed decreased crystallinity and disappearance of polymer peaks upon addition of SiO2. SEM revealed
Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
view for new research in gas sensing application. In this paper we review various two dimensional sheet such as
Graphene, Boron Nitride nanosheet, Mxene and their application in sensing various gases present in the
atmosphere.
METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
most common reason for adulteration is the use of undeclared materials by manufacturers that are cheaper than
the correct and declared ones. The adulterants can be harmful or reduce the effectiveness of the product, or
they can be harmless.
The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
idea from core requires a systematic plan, time management, time investment and most importantly client
attention. The Time required for developing may vary from idea to idea and strength of the team. Leadership to
build a team and manage the same throughout the peak of development is the main quality. Innovations and
Techniques to qualify the huddles is another aspect of Business Development and client Retention.
Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large...YanKing2
Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization. We reported that 1) the reduction ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning and this study can save reduce the cost of invoking GPT-4 by 24%per API query. Importantly, SlimCode simplifies the input code with its greedy strategy and can obtain at most 133 times faster than the state-of-the-art technique with a significant improvement. This paper calls for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.
Unblocking The Main Thread - Solving ANRs and Frozen FramesSinan KOZAK
In the realm of Android development, the main thread is our stage, but too often, it becomes a battleground where performance issues arise, leading to ANRS, frozen frames, and sluggish Uls. As we strive for excellence in user experience, understanding and optimizing the main thread becomes essential to prevent these common perforrmance bottlenecks. We have strategies and best practices for keeping the main thread uncluttered. We'll examine the root causes of performance issues and techniques for monitoring and improving main thread health as wel as app performance. In this talk, participants will walk away with practical knowledge on enhancing app performance by mastering the main thread. We'll share proven approaches to eliminate real-life ANRS and frozen frames to build apps that deliver butter smooth experience.
Encontro anual da comunidade Splunk, onde discutimos todas as novidades apresentadas na conferência anual da Spunk, a .conf24 realizada em junho deste ano em Las Vegas.
Neste vídeo, trago os pontos chave do encontro, como:
- AI Assistant para uso junto com a SPL
- SPL2 para uso em Data Pipelines
- Ingest Processor
- Enterprise Security 8.0 (Maior atualização deste seu release)
- Federated Analytics
- Integração com Cisco XDR e Cisto Talos
- E muito mais.
Deixo ainda, alguns links com relatórios e conteúdo interessantes que podem ajudar no esclarecimento dos produtos e funções.
https://www.splunk.com/en_us/campaigns/the-hidden-costs-of-downtime.html
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-leading-observability-practice.pdf
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-modern-security-program.pdf
Nosso grupo oficial da Splunk:
https://usergroups.splunk.com/sao-paulo-splunk-user-group/
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
Best Practices of Clothing Businesses in Talavera, Nueva Ecija, A Foundation ...IJAEMSJORNAL
This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.
Online music portal management system project report.pdfKamal Acharya
The iMMS is a unique application that is synchronizing both user
experience and copyrights while providing services like online music
management, legal downloads, artists’ management. There are several
other applications available in the market that either provides some
specific services or large scale integrated solutions. Our product differs
from the rest in a way that we give more power to the users remaining
within the copyrights circle.
Social media management system project report.pdfKamal Acharya
The project "Social Media Platform in Object-Oriented Modeling" aims to design
and model a robust and scalable social media platform using object-oriented
modeling principles. In the age of digital communication, social media platforms
have become indispensable for connecting people, sharing content, and fostering
online communities. However, their complex nature requires meticulous planning
and organization.This project addresses the challenge of creating a feature-rich and
user-friendly social media platform by applying key object-oriented modeling
concepts. It entails the identification and definition of essential objects such as
"User," "Post," "Comment," and "Notification," each encapsulating specific
attributes and behaviors. Relationships between these objects, such as friendships,
content interactions, and notifications, are meticulously established.The project
emphasizes encapsulation to maintain data integrity, inheritance for shared behaviors
among objects, and polymorphism for flexible content handling. Use case diagrams
depict user interactions, while sequence diagrams showcase the flow of interactions
during critical scenarios. Class diagrams provide an overarching view of the system's
architecture, including classes, attributes, and methods .By undertaking this project,
we aim to create a modular, maintainable, and user-centric social media platform that
adheres to best practices in object-oriented modeling. Such a platform will offer users
a seamless and secure online social experience while facilitating future enhancements
and adaptability to changing user needs.
Profiling of Cafe Business in Talavera, Nueva Ecija: A Basis for Development ...IJAEMSJORNAL
This study aimed to profile the coffee shops in Talavera, Nueva Ecija, to develop a standardized checklist for aspiring entrepreneurs. The researchers surveyed 10 coffee shop owners in the municipality of Talavera. Through surveys, the researchers delved into the Owner's Demographic, Business details, Financial Requirements, and other requirements needed to consider starting up a coffee shop. Furthermore, through accurate analysis, the data obtained from the coffee shop owners are arranged to derive key insights. By analyzing this data, the study identifies best practices associated with start-up coffee shops’ profitability in Talavera. These findings were translated into a standardized checklist outlining essential procedures including the lists of equipment needed, financial requirements, and the Traditional and Social Media Marketing techniques. This standardized checklist served as a valuable tool for aspiring and existing coffee shop owners in Talavera, streamlining operations, ensuring consistency, and contributing to business success.
Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.
Understanding Cybersecurity Breaches: Causes, Consequences, and Prevention
Extraction of Emoticons with Sentimental Bar
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Extraction of Emoticons with Sentimental Bar
Shreyas Wankhede1
, Ranjit Patil1
, Sagar Sonawane1
, Prof. Ashwini Save2
1
(Computer Engineering, Viva Institute of Technology/ Mumbai University, India)
2
(HOD, Computer Engineering, Viva Institute of Technology/ Mumbai University, India)
Abstract :The latest generation of emoticons which are called as emojis that is largely being used in mobile
communications as well as in social media. In past few years, more than ten billion emojis were used on Twitter.
Emojis which are known as the Unicode graphic symbols, which are basically used as shorthand to express the
concepts and ideas of the people. For smaller number of well-known emoticons, their meanings or sentiments
are well known but there are thousands of emojis so extracting their sentiments is difficult. The Emoji Sentiment
Ranking method which is used to evaluate a sentiment mapping of emojis by using sentiment polarity such as
negative, neutral, or positive. The sentimental classification of tweets with and without emoticons are very much
different.Finally, the method also gives representation of sentiments and a better visualization in the form of a
sentimental Bar.
Keywords-Classification of Emoticons, Emoji Sentiment Ranking, Sentiment Bar, Sentiment labels, Sentiment
score.
1. INTRODUCTION
As the use of social media is increasing day by day, emoticons plays a essential role in communication
through technology, and many other devices have provided different forms of pictures that do not use type
punctuations. They provide another range of expressions and feelings through texting that conveys specific
emotions through facial gestures. Nowadays emoticons on smartphones, in chatting, and in many different
applications, have become tremendously popular worldwide. For example, Twitter has become very active
in sharing content with comments. According to statistics, around 500 million of tweets are dispensed per
day. Each tweet expresses different form of emotions.
An emoticon, such as , and many others are used for facial expression. It also allows the peoples to
express their feelings, moods, and emotions, which replaces and enriches a written message with non-verbal
elements. It allows user to understand the feelings of their friends and colleagues in better manner. Some
social network sites and microblogging tools such as Twitter allows individuals to express their feelings or
opinions to specific results. These short messages which are also known as tweets that includes emotions
such as happiness, sadness, anger etc in it. Classification of emoticons is basically done in two categories
such as positive emoticons and negative emotions. Positive emoticons consistof love and joy whereas
negative emoticons consist of sadness and anger.
The simplest forms of representation are the generally denoted as 'emoticon' or 'smiley'. People
from Japan popularized a kind of emoticon called kaomoji where ((kao)=face and (moji)=characters) [6].
Sentiment analysis of text is being done by many researchers but for emoticons still it’s in developing stage
so it’s a need to research more on emoticons and give it a limelight to know all about emoji for future users.
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2. RELATED WORK
There are six reported works related to Emoticons found in the literature review.
Georgios Solakidis [1]. In this paper the objective is to evaluate sentiment analysis on multilingual
data, also the paper focuses on study and draw conclusion about subjectivity, polarity and the feelings that
is expressed in user generated content, which mainly consist of free text document. The approach involves
detection and use of self-defining features that available within the data that take accounts in two
emotionally rich features: -a] emoticons b] lists of specific keywords. There is machine learning approach
on collection of training data using evaluating and comparing the result of two separate elements that is
emoticons & keywords. There is graphical comparison between keyword and emoticons on subjectivity
level, polarity level. This system integrates and automates all tasks associated with semi-supervised
emotion detection.
Nasiya Najeeb [2]. This paper proposes different opinion mining techniques for various type of natural
language processing for tracking mood of public about particular product or topic. Many peoples write
opinions in forum, microblogging or review websites. This is useful for analysing the data for companies,
governments, and individuals for tracking automatically feelings and attitudes. Social networks allow users
to express their feeling and convey their emotions via text as well as emoticons. Information in terms of text
are extracted ad clustered into emotions and then classified into positive, negative and neutral. An emoticon
basically affects the sentence when it occurs because it also provides better sentiment expression of a web
user. Text extraction is done manually or automatically after extraction the next step is filtration which
includes emoticon replacements. After filtering the data, various classifiers are used to classify text based
on emotions. The algorithm called as Word emotion technique is used to extract emotion form each word.
Types of emoticons: Textual emoticons: - “:”, “=”,“_”, “,” Graphical emoticons: - this provides better
sentiments e.g. I feel very happy. (using phrase), feel very happy . (using phrase as well as emoticon).
Fei Jiang [3]. A decision tree based user’s context classifier and prediction model is designed to
classify tweets according to emoticons expressed through the emoticons. The emoticons which are
proposed by are used for mapping different emotions such as Love, Happiness, Pity, Furious, Heroic,
Fearful, Disgust, Wonder and Peace [3]. These emotions are mandatory part of human nature that can be
considered. The methodology for user’s context personalization based on emoticons involves two major
phases such as: a) Training Phase b) Testing Phase. Along with words, emoticons also extracted. Emoticons
that are extracted from standard library. A decision tree is generated which is based on classifier and
prediction model for performing emotion classification.
Alexander Hogenboom [4]. Nowadays people increasingly use emoticons to express their feelings or
sentiments. People uses emoticons for products, services organizations, individual issues, events topics and
their attributes through social media (Twitter). As twitter have text message limitations to 140 characters.
So, people uses emoji’s instead of big texts to express their sentiments. Emoticons are ASCII art emoticons
are also called as smileys. Emoticons always adds essence for plain text and convey joy , sadness ,
laughter etc. To exploit or to understand emoji’s in automated system first need to analyse that emoticons
can typically relate to sentiments of the data in which they occur. They affect sentences or paragraphs.
Some paragraphs contain only one emoticon which shows different sentiments, but in other paragraphs
there are multiple use of emoticons so it will affect the sentence in which it occurs. Till now textual based
sentiments were used in twitter. But now people uses emoticon to express their feelings so now it will be
based on lexicon based sentiment analysis for emoticons.
Anthoniraj Amalanathan [5]. They proposed Emoticons Space Model (ESM) for sentiment analysis. In
this paper the ESM technique treats each emoticons differently and also integrates that do not have clear
emotional meaning. ESM simplifies emoticon signals and consistently performs previous state operations.
ESM consist of two phase: -a) Projection Phase:-obtain co-ordinates of the posts are obtained based on
coordinate of words. b) Classification Phase: -Use co-ordinate of the posts as features for supervised
sentiment classification task.
Maryam Hasan [6]. In social media there many tools are widely used by personally to express their
feeling and comments in the form of text message. Detection of emotions in plain text has a wide range of
applications which includes human individual emotions and also public emotions of other people. They
propose new approach in which classifying text messages automatically according their emotional states.
There is one of the model studied that Circumflex Emotional model. This model characterized along two
dimensions a) Valence b) Arousal. The Twitter messages are selected as input data set to the system and in
that data set they provide a very large amount of available group of emotions. Main thing is they used
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supervised classifier for to detect classes of multiple emotions. In detection of emotions from the text
messages there are some problem such as sparse and high dimensional feature vectors of messages. For the
tackle of those problem they utilize Lexicon of emotions which uses these steps Designing and
implementing a method that automatically label twitter messages based on the emotions of their authors,
then Resolving the problem of high dimensional feature space in twitter dataset and lastly Achieving
highest accuracy for classifying twitter messages based on their emotional states. The accuracy is compared
with several machine learning algorithms and methods such as SVM, KNN, Decision Tree and Naive
Bayes.
3. PROBLEM DEFINITION
The new generation of emoticons which are known as emoji's that is increasingly being used in mobile
communications as well as in social media. For smaller number of popular emoticons, their sentiments are
well known but there are thousands of emoji's so extracting their sentiments is challenging. The existing
system for exploiting emoticons, classification of emoticons was based only on sentiment score and polarity
but did not use Sentiment Rank and Bar which will provide better understanding of emoticons.
4. METHODOLOGY
The method proposed in this paper aims for automation of sentiment analysis for emoticons. It uses two
main approaches first one is the emoji sentiment lexicon which calculates the sentiment score and the
second one is emoji sentiment ranking which considers ranking and positions of the emoticons for
sentiment analysis.Proposed system also gives graphical representation on sentiments after extracting
emoticons. Fig 4.1: shows System flow diagram for proposed system.
Fig 4.1: System flow diagram of proposed model.
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The sentiment of emoticons is evaluated from the sentiment of tweets. In the training phase initially
labelling of the tweets will be done. Sentiment labels can take one of the three values which are negative, neutral
and positive. A label ‘c’ is discrete three valued variables {-1, 0, +1} [5]. After this extraction of emoticons are
done from the tweets by separating the text from emoticons. Then the emoticons are stored in the dataset for
classification and further process. When the user enters the tweet in the text field of Twitter the content classifier
is used for classifying the text and emoticons in that tweet with having access to the dataset. In classification,
Emoji Sentiment Ranking uses the overall mapping of emojis. The position of an emoji is determined by
sentiment score 𝑆and neutrality p0. The sentiment score will be in the range of (-1, +1) and computed as p+ to
p-. The positive emojis will be towards right hand side with green colour, negative ones towards left hand side
with red colour and the neutral emojis with yellow colour.
The frequently used emoticons are given a higher rank and less frequently emoticons are given as
lower rank while others are given as mid-point rank. Emojis, on the other hand, can appear in groups and also at
the end of the tweets. In the sentiment distribution for the set of relevant tweets the system will find the number
of occurrences of emoticons in the tweets, and also the sentiment label c by using discrete probability
distribution formula. After finding Sentiment score and Rank based on position the next step is to form
Sentiment Bar. The sentimental bar is a useful for proper visualization of the sentiments attached to an emoji.
The sentimental bar will include all the properties of emoticons such as p-, p0, p+ and 𝑆. The coloured bar
extends from −1 to +1, which is the range of the sentiment score (red, yellow, green). The grey bar is centred at
𝑆 and is extended, but never beyond the range of 𝑆and gives the Sentiment Bar for analysis of different
emoticons whether they are having positive, neutral or negative sentiment.
The Fig 4.2 shows the Sentimental Bar from extraction of emoticons. When user will enter the tweet in
the text field of twitter, this tweet may consist of text and emoticons. It is necessary to extract the emoticons
from tweet. The content classifier will extract the emoticons from the tweet and separates emoticons from text.
For example, if user enters emoji with smile face as shown in Fig 4.2 the labelling of this emoji is done in the
training phase with positive value. Then the sentiment score is assigned to it which is in the range of -1 and +1.
Based on the occurrence and number of counts of this emoji, the ranking of this emoji is done by using
probability distribution. Finally, a Sentimental Bar is generated for the emoji as shown below.
Fig 4.2: Sentimental Bar
5. EXPECTED RESULT
The occurrence and position of emoticons matters a lot for the prediction of sentiment analysis and
hence the proposed system includes Sentiment Rank and Position Approach for better sentiment analysis. A
graphical representation for sentiments of tweets in a form of Sentimental Bar for easy analysis of sentiments
which represents in the form of red-negative, yellow neutral & green-positive. The Emoji Sentiment Ranking
will be an important resource for helping humans during the representation process, or even for the
automatically labelling of tweets with emojis for sentiment analysis.
6. CONCLUSIONS
This model describes the construction of an Emoji Sentiment Lexicon and the Emoji Sentiment
Ranking for different emoticons in tweets based on their occurrence. The Emoji lexicon method can also be
used for grouping the emoticons with a sentiment including the text. The Emoji Sentiment Lexicon and Emoji
Sentiment Ranking approaches will be constructed for different emoticons used in the tweets based on their
occurrence for predicting better sentiments. This approach has analysed and used the sentiment properties of the
emojis in depth and also gives some interesting facts regarding emoticons. In future, it will be interesting to
monitor and analyse how fast the usage of emojis are growing in communication, and whether textual
communication will be replaced with different technique. Till now many researchers have focused on text based
sentiment analysis but have not given much priority for emoticon sentiments so the proposed system focuses on
emoticon sentiments and thus generates sentimental bar for it.
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