Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
This document provides an overview of sentiment analysis on Twitter. It discusses how sentiment analysis can be used to determine sentiment behind texts and social media updates. The document outlines the methodology used, including data collection from Twitter, preprocessing, feature extraction, and using classifiers like Naive Bayes to predict sentiment. It also discusses applications of sentiment analysis and future areas of improvement, such as using different algorithms and temporal analysis. The goal is to more accurately analyze human sentiment from social media data.
This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.
Sentiment classification for product reviews (documentation)
The documentation of the pre-master graduation project prepared by my self and my colleagues Mostafa Ameen, Mai M. Farag and Mohamed Abd El kader.
If you want me to conduct any similar research for you you can have my service through this link: https://www.fiverr.com/meizzo/convert-your-textual-data-set-from-csv-file-format-to-arff-format-for-weka
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
System Analysis & Design Presentation.pdfAriful Islam
Sentiment analysis is the process of analyzing digital text to determine if the emotional tone of the message is positive, negative, or neutral. Today, companies have large volumes of text data like emails, customer support chat transcripts, social media comments, and reviews. Sentiment analysis tools can scan this text to automatically determine the author's attitude towards a topic. Companies use the insights from sentiment analysis to improve customer service and increase brand reputation.
This document is a project report submitted by four students - Anil Shrestha, Bijay Sahani, Bimal Shrestha, and Deshbhakta Khanal - to the Department of Electronics and Computer Engineering at Tribhuvan University in partial fulfillment of the requirements for a Bachelor's degree in Computer Engineering. The report details the development of a web application called "Tweezer" to perform sentiment analysis on tweets in order to determine public sentiment towards various products, services, or personalities. Literature on previous work related to sentiment analysis, especially on social media data like tweets, is also reviewed in the report.
This document provides an overview of sentiment analysis on Twitter. It discusses how sentiment analysis can be used to determine sentiment behind texts and social media updates. The document outlines the methodology used, including data collection from Twitter, preprocessing, feature extraction, and using classifiers like Naive Bayes to predict sentiment. It also discusses applications of sentiment analysis and future areas of improvement, such as using different algorithms and temporal analysis. The goal is to more accurately analyze human sentiment from social media data.
This document discusses sentiment analysis on Twitter data using machine learning classifiers. It describes Twitter sentiment analysis as determining if a tweet is positive, negative, or neutral. Some challenges are that people express opinions complexly using sarcasm, irony, and slang. The document tests different classifiers like Naive Bayes and SVM on Twitter data preprocessed by tokenizing, extracting sentiment features, and part-of-speech tagging. It finds that extracting more features like sentiment and part-of-speech tags along with an SVM classifier achieves the best accuracy of 68% at determining tweet sentiment.
Sentiment classification for product reviews (documentation)Mido Razaz
The documentation of the pre-master graduation project prepared by my self and my colleagues Mostafa Ameen, Mai M. Farag and Mohamed Abd El kader.
If you want me to conduct any similar research for you you can have my service through this link: https://www.fiverr.com/meizzo/convert-your-textual-data-set-from-csv-file-format-to-arff-format-for-weka
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
This is small twitter sentiment analysis project which will take one keyword(which is the primary way of storing the tweet in Twitter) and number of tweets, and gives you the pictorial representation of the overall sentiment.
This document discusses a presentation given at the Sentiment Analysis Symposium in San Francisco in October 2012. The presentation introduces opinion mining and sentiment analysis, covering key concepts, applications, challenges, and techniques. Some of the main topics discussed include defining opinion mining and sentiment analysis, analyzing public mood and opinions on social media, predicting future trends from social data, and addressing challenges like determining the credibility and trustworthiness of opinions.
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.
This document discusses sentiment analysis. It defines sentiment analysis as analyzing text to determine the writer's feelings and opinions. It notes the rapid growth of subjective text online and how businesses and individuals can benefit from understanding sentiments. It describes common applications like brand analysis and political opinion mining. It also outlines different approaches to sentiment analysis like using semantics, machine learning classifiers, and sentiment lexicons. The document provides an example implementation and discusses advantages like lower costs and more accurate customer feedback.
Sentiment analysis is the use of natural language processing, statistics, or machine learning to identify and extract subjective information from text sources. It can determine whether the sentiment of a text is positive, negative, or neutral. Approaches to sentiment analysis include using machine learning algorithms like naive Bayes classifiers, maximum entropy classifiers, and SVMs. Tools for sentiment analysis include WEKA, Python NLTK, RapidMiner, and LingPipe. The future of sentiment analysis may include increased accuracy that rivals human-level processing, continued improvement in machine learning techniques, interpreting more subtle human emotions, and powering predictive analytics applications.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
Sentiment analysis and opinion mining is almost same thing however there is minor difference between them that is opinion mining extracts and analyze people's opinion about an entity while Sentiment analysis search for the sentiment words/expression in a text and then analyze it.
It uses machine learning techniques like SVM (Support Vector Machines) to analyze the text and classify them as positive, negative or neutral.
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
The document outlines a procedure for performing sentiment analysis on tweets. It involves using the Twitter API and streaming tweets containing a keyword. The tweets are preprocessed by filtering, tokenization, and removing stop words. Then a classification algorithm is applied to classify each tweet as positive, negative, or neutral sentiment. Finally, the results will be plotted to analyze the polarity of sentiments in the tweets.
IRJET- A Review on: Sentiment Polarity Analysis on Twitter Data from Diff...IRJET Journal
This document summarizes research on sentiment polarity analysis of Twitter data from different events. It discusses how Twitter data can be used for opinion mining and sentiment analysis. Several papers that used techniques like naive Bayes classifier, support vector machines, and dual sentiment analysis on Twitter data are summarized. The document also provides an overview of the key steps involved in a Twitter sentiment analysis system, including data collection, preprocessing, feature extraction, training a classification model, and evaluating accuracy. The goal of analyzing sentiments on Twitter is to understand public opinions on different topics and events.
Combining Lexicon based and Machine Learning based Methods for Twitter Sentim...IRJET Journal
This document discusses combining lexicon-based and machine learning methods for Twitter sentiment analysis. It first describes lexicon-based approaches like TextBlob and Vader that use sentiment lexicons to determine tweet polarity. It then discusses machine learning approaches like random forest, support vector machines, and decision trees that are trained on labeled tweet data. The document finds that a random forest classifier achieved the highest accuracy of 99.92% at predicting tweet sentiment, demonstrating the effectiveness of combining both lexicon-based and machine learning methods for Twitter sentiment analysis.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
This document describes a system to predict customer purchase intention from social media posts like tweets. The system was developed using a dataset of 3,200 manually annotated tweets relating to the iPhone X. Various machine learning models were tested on their ability to classify tweets as indicating purchase intention or not. The models were evaluated based on accuracy, precision, recall, and F-measure. The best performing models were logistic regression with a binary document vector, achieving an accuracy of 80.8%, and SVM with a TF document vector, achieving 80.5% accuracy. The system aims to help companies better target advertising to potential customers based on analysis of their social media data.
IRJET- Analysis of Brand Value Prediction based on Social Media DataIRJET Journal
This document presents a study that analyzes brand value prediction based on social media data using different sentiment analysis techniques. The study compares lexicon-based sentiment analysis tools SentiWordNet and TextBlob, and also evaluates supervised machine learning classifiers Naive Bayes and CNN. The CNN model achieved the highest accuracy of 94.4% when applied to a dataset of Amazon product reviews, outperforming the Naive Bayes model which achieved 82% accuracy. The study concludes that hybrid methods combining lexicon-based and machine learning approaches can effectively analyze sentiment from large social media datasets.
The document discusses a content-based recommendation system with sentiment analysis. It provides an overview of recommendation systems and their importance. The objectives are to provide personalized recommendations to users based on their preferences using information filtering techniques. Existing systems faced issues like scalability, sparsity, and cold starts. The proposed system is a hybrid approach that combines item-based collaborative filtering with user clustering to make predictions. It will be scalable while addressing cold starts. Tools like Flask, JavaScript, Python are used. Cosine similarity and sentiment analysis techniques are also discussed. The conclusion is that the proposed system can recommend less popular items and future work could include other factors in recommendations.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
This document discusses sentiment analysis of tweets from Twitter. It begins with an introduction to how social media allows people to share opinions and how analyzing sentiment can be useful. It then discusses previous work on sentiment analysis of Twitter data, focusing on techniques like Naive Bayes classification. The document outlines a proposed approach to collecting Twitter data using APIs, preprocessing the data by removing stop words and emoticons, and classifying sentiment using Naive Bayes. Finally, it discusses applications of sentiment analysis and potential areas for future work, such as handling multiple languages and semantic analysis.
This document summarizes a study that used machine learning methods to analyze sentiment reviews for Indian Railways from Facebook data. The study tested several classifiers including SVM, Naive Bayes, Random Forest, Decision Tree, and K-NN. It found that K-NN performed best with the lowest false positive rate. The study evaluated the classifiers using metrics like accuracy, precision, recall, F-measure, and logarithmic loss. K-NN achieved the best results for most metrics according to the experimental results presented in the tables.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
This document summarizes research on sentiment analysis of Twitter data. It discusses how sentiment analysis can classify tweets as positive, negative, or neutral. It reviews different techniques for sentiment analysis, including machine learning approaches like Naive Bayes classifiers and lexicon-based approaches. The document also describes prior studies that have used sentiment analysis techniques to predict security attacks based on Twitter sentiment and explore improvements in classification accuracy. In general, the document outlines common methods for analyzing sentiment in social media data and highlights past applications of the analysis.
IRJET - Sentiment Analysis of Posts and Comments of OSNIRJET Journal
This document summarizes a research paper that aims to perform sentiment analysis on posts and comments on online social networks like Twitter. The proposed system seeks to identify the sentiment behind content posted to determine if users exhibit signs of depression. It will analyze text for positive emotions like happy and negative emotions like sad using machine learning techniques. The results will then classify the degree of negative sentiment and potential depression displayed by the user. The system architecture involves collecting social media data, filtering out noise, comparing text to stored emotional words, and generating a result that calculates sentiment scores and ranks emotions displayed in the content.
Emotion Recognition By Textual Tweets Using Machine LearningIRJET Journal
This document discusses using machine learning techniques to perform sentiment analysis on tweets in order to predict election results in India. It begins with an introduction to sentiment analysis and how it can be applied to social media tweets. It then discusses existing methods for sentiment analysis that have certain disadvantages. The proposed system aims to improve accuracy by using techniques like Naive Bayes classification, support vector machines, decision trees, and long short-term memory networks. It presents the system design, implementation details using Python and various machine learning algorithms, and testing of the system to classify tweets by emotion and predict election outcomes.
IRJET- Interpreting Public Sentiments Variation by using FB-LDA TechniqueIRJET Journal
This document discusses sentiment analysis techniques for classifying tweets based on their positive, negative, or neutral sentiment. It proposes two Latent Dirichlet Allocation (LDA) based models - Foreground and Background LDA (FB-LDA) and Reason Candidate and Background LDA (RCB-LDA) - to analyze sentiment variation in tweets. FB-LDA can filter background topics and extract foreground topics to identify possible explanations for sentiment changes. RCB-LDA can rank reason candidates expressed in tweets to provide sentence-level sentiment explanations. The proposed techniques are intended to classify tweets and evaluate public sentiment variations by extracting possible reasons for those variations.
IRJET - Twitter Sentiment Analysis using Machine LearningIRJET Journal
This document summarizes a research paper on Twitter sentiment analysis using machine learning. It describes extracting tweets on a topic, cleaning the data, extracting features, building a logistic regression model to classify tweets as positive, negative or neutral sentiment, and validating the model. The goal is to analyze public sentiment from Twitter data, which has applications in marketing, product feedback, and other areas.
A Intensified Approach On Enhanced Transformer Based Models Using Natural Lan...IRJET Journal
This document discusses and compares two neural network transformer models, BERT and ERNIE, for sentiment analysis. BERT uses bidirectional training of language representations to learn contextual relations between words. ERNIE enhances BERT by integrating knowledge from lexical, syntactic and semantic data during training. The document analyzes how ERNIE uses different masking techniques compared to BERT to better model semantic relationships between words and entities. Experimental results on product review datasets show ERNIE achieves better performance than BERT for sentiment classification tasks.
IRJET - Support Vector Machine versus Naive Bayes Classifier:A Juxtaposition ...IRJET Journal
This document compares the Naive Bayes and Support Vector Machine machine learning algorithms for sentiment analysis. It discusses how each algorithm works, including vectorization, parameter tuning, and terminology related to evaluating model performance such as bias, variance, cross-validation, and ROC curves. An experiment is described that applies both algorithms to movie, product, and service reviews from public datasets to determine which performs better for sentiment classification based on various evaluation metrics like accuracy, precision, recall and F1 score. The results are analyzed to understand which algorithm may be better suited for different use cases and how future work could improve model performance.
IRJET- Physical Design of Approximate Multiplier for Area and Power EfficiencyIRJET Journal
This document summarizes research on using statistical measures and machine learning techniques to perform sentiment analysis on product reviews. The researchers collected product review data from online sources and analyzed the sentiment and opinions expressed in the text using support vector machine classifiers. They classified reviews as positive or negative and analyzed key product features that were discussed. The results demonstrated that statistical sentiment analysis can help companies better understand customer feedback and identify popular product versions or attributes. Several related works applying techniques like naive Bayes, lexicon-based methods and aspect-based sentiment analysis on reviews from domains like movies, hotels and restaurants are also summarized.
IRJET- Classifying Twitter Data in Multiple Classes based on Sentiment Class ...IRJET Journal
This document presents a proposed model for classifying Twitter data into multiple sentiment classes using machine learning techniques. The model first preprocesses the Twitter data by removing stop words and special characters. It then applies a negation filter to group the data into positive and negative classes based on the presence of negation words. Natural language processing is used to extract part-of-speech features from the text, transforming it into a structured format. The support vector machine classifier is trained on the labeled data and used to predict the sentiment class of new text data. The model's performance is evaluated based on accuracy, error rate, memory usage, and time consumption, demonstrating that it can accurately classify Twitter data into multiple sentiment classes.
Similar to Methods for Sentiment Analysis: A Literature Study (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
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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
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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:
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METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
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compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
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The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
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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
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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.
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A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
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.
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Online music portal management system project report.pdfKamal Acharya
The iMMS is a unique application that is synchronizing both user
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This was our 9th Sem Design Studio Project, introduced as Conservation of Taksar Bazar, Bhojpur, an ancient city famous for Taksar- Making Coins. Taksar Bazaar has a civilization of Newars shifted from Patan, with huge socio-economic and cultural significance having a settlement of about 300 years. But in the present scenario, Taksar Bazar has lost its charm and importance, due to various reasons like, migration, unemployment, shift of economic activities to Bhojpur and many more. The scenario was so pityful that when we went to make inventories, take survey and study the site, the people and the context, we barely found any youth of our age! Many houses were vacant, the earthquake devasted and ruined heritages.
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How to Manage Internal Notes in Odoo 17 POSCeline George
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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.
21CV61- Module 3 (CONSTRUCTION MANAGEMENT AND ENTREPRENEURSHIP.pptx
Methods for Sentiment Analysis: A Literature Study
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Methods for Sentiment Analysis: A Literature Study
Shiv Dhar1
, Suyog Pednekar1
, Kishan Borad1
, Ashwini Save2
1
(B.E. Computer Engineering, VIVA Institute of Technology, University of Mumbai, India)
2
(Head of Department, Computer Engineering, VIVA Institute of Technology, University of Mumbai, India)
Abstract : Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Keywords– Machine Learning, Sentiment Analysis, CNN, analysis, AI, SVM, NLP.
1. INTRODUCTION
Sentiment analysis intents to define the attitude of a speaker, writer, or other subject with respect to some
topic or the overall contextual division or emotional response to a document, interaction, or event. It refers to the
use of natural language processing, text analysis, computational semantics, and biometrics to systematically
identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is broadly
applied to “voice of the customer” materials such as reviews and survey responses, as well as to online and
social media. Sentiment analysis has claims in a variety of domains, ranging from marketing to customer service
to clinical medicine.
Sentiment analysis stands at the intersection of natural language processing and large-scale data mining.
Sentiment analysis has important applications in academia as well as commerce. The understanding of human
language is a core problem in AI research. At the same time, with increasingly lowering barriers to the Internet,
it is easier than ever for end-users to provide feedback on the products and services they use. This information is
highly valuable to commercial organizations; however, the volume of such reviews is growing rapidly,
necessitating an automated approach to extracting meaning from the huge volume of data. This automated
approach is provided by sentiment analysis.
In this paper, various approaches to sentiment analysis have been examined and analysed. Techniques such
as lexicon based approach, SVM [4], Convolution neural network [9], morphological sentence pattern model
and IML algorithm are discussed. These techniques all have different strengths and weaknesses, and are have
different use cases. Their advantages and disadvantages are explored and compared.
2. SENTIMENT ANALYSIS TECHNIQUES
2.1 Sentiment Analysis on Twitter using Streaming API [8]
The propose system focuses on analyzing what people thinks about various products and services.
Many users share their opinions about various products and contents. Sentiment analysis helps in classifying the
positive or negative data. In the proposed system, it uses Natural language processing - NLTK, where it helps in
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tokenization, stemming, classification, tagging, parsing and sentiment reasoning. Its basic feature is to convert
unstructured data into structured data.
It uses Naive Bayes for classification which requires number of linear parameters. The system uses
Hadoop for extracting information and uses Twitter Application Programming Interface. The system gathers
streaming tweets using Twitter API and assigns every tweet a positive or negative probability.
The future system mainly focuses on real time sentiment analysis such as evaluating tweets from twitter. It
performs sentiment analysis, feature based classification and opinion summarization.
Advantages:
Classification is done in real time which makes it very efficient.
Sentiment analysis in this system uses Hadoop to load live data.
Several systems use stored tweets for classification, leading to high requirement of space, whereas here the
storage required is less.
Disadvantages:
While classifying, the words are accepted individually rather than in a fix pattern or complete sentence.
The semantic meaning is neglected as single words are scanned.
2.2 Neural Networks for sentiment analysis on twitter [7]
The proposed system mainly focuses on providing polar views by dividing the opinions in social media
and websites having customer reviews. It divides the reviews from websites and divide them into positive,
negative and neutral reviews. The system used feed forward neural network. The neural network used is on
MATLAB, using neural network toolbox. It reduces the input by removing the punctuations, single characters,
stop words like and, to, the etc. and also mentions to other users using @ symbol.
The system uses Porter’s stemming algorithm for stemming. Each tweet obtained is assigned a value
and arranged linearly in 2D table. It uses pattern matching in neural networks for checking the data.
The proposed system performs sentiment analysis on twitter using neural networks. Sentiment analysis is
performed by various methods, here it uses neural networks which helps in achieving more accuracy and
efficiency. Preprocessing is also implemented by the proposed system which helps in obtaining better results.
Advantages:
Tweets are easily classified into positives and negatives.
Preprocessing helps in improving the time required.
Reducing redundant data helps in gaining better accuracy.
Disadvantages:
The input is still comparatively large and thus require more time.
The input on twitter has #, which are connected having no space. This requires dividends and thus needs to be
implemented in future.’
2.3 Product related information sentiment-content analysis based on Convolution Neural
Networks for the Chinese micro-blog [9]
Sentiment analysis is performed by the suggested system on various Chinese micro-blogs. It performs
sentiment analysis to determine whether positive/negative or it is an advertisement. The system uses convolution
neural networks for classification. And support vector machine algorithm (SVM algorithm) is used. It reduces
the size of input data by breaking down major data set containing all the information into smaller data set by
removing unwanted data like author's name, duplicated data and similar texts. It uses CNN, which has four
layers namely input layer, convolution layer, pooling layer and fully connected layer. SVM and lexicon analysis
is used as baseline.
In the proposed system sentiment analysis of Chinese micro-blogs is performed using CNN. There are
many product advertisements and promotions in micro-blogs which can be detected using this process. The
system is quite useful in removing the redundant data such as advertising and promotions, resulting in better
results for sentiment analysis.
Advantages:
It provides better result than earlier lexicon analysis.
Along with positive and negative statement, it also determines advertisement.
It also provides better result than SVM.
Disadvantages:
It takes an entire sentence or concatenated sentence as input. Thus, more time is required for analysis.
Here entire data results in more time required to analyze than just predefined patterns.
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It uses sentence embedding viz. less effective than character embedding.
2.4 Convolution Neural Networks based sentiment analysis using Adaboost combination [6]
The proposed system focuses on feedback analysis. This paper states various techniques for sentiment
analysis classification. It includes SVM, Naive Bayes, recursive neural network, auto encoders. It also states
various methods for identifying the different roles of specific N-grams. It uses Adaboost to combine different
classifiers.
The system proposes preprocessing using sentence matrix input, i.e. the input matrix in classified and
used as input in a matrix. Here N-grams are formed which are used for dividing into smaller segments according
to the N. It uses Adaboost algorithm to combine week classifiers with the strong classifiers. Parameter
overfitting is checked and reduced using regularization. It drops certain parameters which are not defined
earlier. The data set used in this system are Movie Review and IMDB.
Advantages:
It uses boosted CNN to provide better results than general CNN.
The proposed model separates the features by passing the documents and then boost the classifiers trained on
these representations.
Disadvantages:
Although it uses N-gram approach, it still must cover the total input and thus time required is high.
There can be more layers added in CNN for better result.
2.5 A Feature based approach for sentiment analysis using SVM and Co-Reference Resolution
[4]
Online shopping is trending these days as it’s found secure. People buy products online and post their
reviews on it. These are in the form of tweets or product reviews. It is difficult to manually read these reviews
and assign sentiment to them. So, for all these tweets an automated system can be created which will analysis
the review and extract the user percepts. In this paper they have developed a producer for feature based
sentiment analysis by a classifier called Support Vector Machine.
In this paper they have used machine learning approach called supervised classification which is more
accurate than all other methods as the classifier is to be trained using the real-world data set. They used
SentiWordNet which is created mainly for opinion mining. As every word in SentiWordNet have 3 polarities as
Positive, Negative and Subjective. SVM is used because sentiment analysis is a binary classification and it has
capability to work on huge datasets. Co-Reference Resolution is to remove the repetitions of words in a sentence
and to develop the relation between two sentences for the sentiment analysis.
Advantages:
Combination of SVM and Co-Reference Resolution improves the accuracy of feature based sentiment analysis.
SentiWordNet helps to find the Polarities of the opinion words.
Disadvantages:
Sarcastic reviews are different to identify for computer as well as human.
Some reviewers may spam the reviews which is different to identify.
Reviewers may post advertisement which is to be detected and discarded.
2.6 SentiReviews: Sentiment analysis based on Text and Emoticons [5]
Sentiment using emoticons is increasing gradually on social networking. People comment, tweets post
their opinions using test as well as emoticons. Which increase the difficulties in analysis the sentiment of the
reviewer. Various factors that affect sentiment analysis are discussed here but the focus is on the emoticons and
the role of emoticons in sentiment analysis also various issues like sarcasm detection, multilingualism handling
acronyms and slang language, lexical variation and dynamic dictionary handling are discussed. Users these days
use emoticons to express most of their emoticons, text communication erase the uses of emoticons.
Sentiment analysis can be done based on two approaches, Lexicon based approach and Machine Learning
approach. The Lexicon is the vocabulary of person, language or branch of knowledge used for calculating
polarities of sentiment, in the Lexicon based approach. In Machine Learning approach, approach the machine/
computer learns the sentiment on regular bases and the polarities are assign.
Advantages:
Various methods are available to find the sentiment in a tweet or review.
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Various approaches can be used to detect the sentiment from the feature based sentiment analysis.
Disadvantages:
Emoticons in the sentences, tweets or review is the problem to define the sentiment.
Dealing with the variation of lexicon can be challenging task in sentiment.
2.7 A feature based approach by using Support Vector Machine for sentiment analysis [10]
As the modern era of globalization, e-commerce is growing in vast number so as their opinion also, but
it is very difficult to identifying whether it is positive, negative or neutral and it would be tiresome job to study
all the opinion manually. To find out the sentiment an automated system must be developed “Support Vector
Machine” can be used for this method. SVM is machine that takes the input and store them in a vector then
using SentiWordNet it scores it decides the sentiment. It also classifies the opinion in overall way by positive,
negative or Neutral.
Advantages:
The accuracy rate is increased as each word in the opinion are scored and the overall sentiment is given.
It can work on large data a single time.
Disadvantages:
Sarcasm detection can be a problem.
Anaphora Resolution is most user ignores the pronouns.
Grammatical mistakes of user.
2.8 Localized twitter opinion mining using sentiment analysis [11]
As the public information from social media can get interesting result and the public opinion on any
product. Service or personally is most effective and it is necessary to find this information from social media.
Sentiment analysis mining using Natural Language Processing, Rapid miner, SentiWord, SNLP as mining of all
the opinion on social media has become a necessity for the analysis of sentiment from the user. Stanford NLP is
used to extract the sentiment from an opinion, Rapid Miner is used to mine all the opinion, tweets from the
social using a keyword, SentiWord is used to assign the polarities to the keywords in the opinion.
Advantages:
Mining of opinion using keyword of product is done faster.
Polarities assignment helps to analysis the opinion.
Disadvantages:
Emoticons used in tweets can be difficult to result the sentiment.
Co-reference Resolution in opinion must be serious issue
2.9 A Method for Extracting Lexicon for Sentiment Analysis based on Morphological Sentence
Patterns [1]
Aspect-based sentiment analysis is higher-quality and more in-depth than, the probability-based model.
But building the aspect-expression pairs is a challenge (manually is slow, obviously). An unsupervised approach
to building aspect-expression pairs is proposed. The natural morphological (i.e. grammatical) patterns in
sentences are exploited to build aspect-expression pairs. It uses POS tagging to find expression candidates for
aspects. Thus, it builds aspect-expression pairs which are then analyzed for sentiment.
Advantages:
The biggest advantage is that aspect-based sentiment analysis can be done automatically, in an unsupervised
manner.
This helps us scale this in-depth approach to large datasets and new data, without human intervention.
It does so while maintaining or increasing classification accuracy.
Disadvantages:
The aspect-based approach is an all-or-nothing approach.
That is, if an aspect-expression pair is found, then results are usually quite accurate.
But if no aspect-expression pair is found, then that review or tweet cannot be processed further, rendering it
effectively into useless noise.
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2.10 Sentiment Analysis Using Machine Learning and Lexicon-based Approaches for Student
Feedback [2]
Evaluation of instructors and courses at the end of the semester is becoming a standard practice. Along
with scale-based feedback, students also provide textual feedback about the courses; this feedback can be
analyzed for sentiment.
The paper recommends a hybrid methodology to sentiment analysis using both machine learning and
lexicon-based approaches. This hybrid methodology yields an enhanced result than the lexicon-based approach
or machine-learning approaches alone.
System Used:
The process methodology is as follows:
1. Dataset Description:
The dataset was manually labelled as positive, negative, neutral.
Thus, it is a supervised dataset.
2. Preprocessing:
The Python NLTK package was used to perform preprocessing: punctuation, tokenization, case conversion, stop
words.
3. Data Partition:
The training test ratio of the dataset was 70:30.
TF-IDF (Term Frequency - Inverse Document Frequency).
The words that occur frequently in the dataset but not in a ‘neutral’ corpus are assigned a higher weight.
N-gram Features, Unigram (1-word) and bigram )2-word) features were extracted.
Lexicon Features, the semantic orientation was determined using an existing sentiment dictionary.
4. Model Training:
The hybrid model for sentiment analysis was trained using unigrams, bigrams, TF-IDF and lexicon-based
features.
To train the model, random forest and support vector machine (SVM) algorithms were used.
This paper yielded a marginally better result than purely lexicon-based approaches.
It outperforms many commercial implementations such as Microsoft’s API, Alchemy, and Aylien.
2.11 A Context-based Regularization Method for Short-Text Sentiment Analysis [3]
The authors suggest a fusion model that combines word-similarity knowledge and word-sentiment
knowledge. They use the contextual knowledge obtained from the data to improve classification accuracy.
System:
To compute the sentiment polarity of a word, TRSR (TextRank Sentiment Ratio) is used. Word-
embedding is used to compute the similarity between words.
This contextual knowledge obtained is not statistical but on the semantic level.
These two regularizations are combined as a classification model, which converts it to an optimization problem
which can be solved computationally.
The parameter obtained from training the model applies into the logistic regression, and we get the
final classification model. The hybrid model that combines word-similarity and word-sentiment performs better
than either of the approaches used individually.
2.12 Aspect-based Feature Extraction and Sentiment Classification using Incremental Machine
Learning Algorithm for Review Datasets [12]
The paper offers an approach for sentiment analysis using a planned iterative decision tree. Customer
reviews are collected and from them, sentiments and aspects are identified; this is called aspect-based feature
extraction. The authors compare the performance of their proposed system with baseline machine learning
algorithms like SVM and naive Bayes.
There are 3 stages in this system:
1. Data preprocessing.
Many preprocessing stages are used to remove irrelevant and noisy data.
2. Aspect-based sentiment analysis.
The aspects and expressions are identified. Sentiment analysis will be performed on these aspects.
3. Opinion summarization using iterative learning tree algorithm.
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It uses an iterative practice to categorize the given inputs for the assessment of sentiment. It starts comparison
from the root node and then compares it with every instance of data. Labels are assigned to the leaf nodes. Every
node in the tree represents an aspect.
Advantages:
This approach performs better than other algorithms like naive Bayes and SVM.
An incremental decision tree is much faster and better than a linear decision tree due to reduced memory and
limited buffer requirements.
Disadvantages:
Classification, while better, is nevertheless supervised, because he class labels need to be well-defined.
This means that it cannot be used for new, unstructured datasets.
3. ANALYSIS
Following table is a summary of studied research papers on Sentiment analysis and various techniques used.
Sr.
No.
Title Technique Used Dataset Accuracy
1 Sentiment analysis of student
feedback using machine
learning and lexicon based
approaches [2]
It uses a hybrid model.
It integrates TF/IDF +
lexicon with the machine
learning algorithms like
Random Forest and SVM.
1230 comments
from the institute's
education portal.
Accuracy:
0.93
F-measure:
0.92
Improved by
0.02
2 A context-based regularization
method for short-text
sentiment analysis [3]
It uses a classification model
that combines two
regularizations, word
similarity and word
sentiment.
Introduces new word-
sentiment calculating.
Movie comments
from Cornell Univ.
2016 US election
comments crawled
from Facebook.
S.C.D. from
Weibo.
Accuracy is
improved by
over 4.5%
baseline
3 A feature based approach for
sentiment analysis using SVM
and coreference resolution [4]
SVM for classification from
huge dataset.
Coreference resolution to
extract the relation from two
sentences.
Reviews from
ecommerce sites
Combining
coreference
and SVM, it
improves the
accuracy of
feature-
based
sentiment
analysis
4 SentiReview: Sentiment
analysis based on text and
emoticons [5]
Lexicon-based approach for
assigning polarities.
Machine learning approach
for constantly analyzing the
polarities.
Comparison for between
different methods to
analyses sentiment
polarities.
Twitter API
Weibo
Stating
various
methods
5 Convolutional Neural Network
based sentiment analysis using
Uses boosted CNN Model. Movie Review Accuracy is
89.4%
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adaboost combination [6] Adaboost algorithm is used
to regularize.
IMDB
Increased by
0.2%
6 Neural Network for Sentiment
analysis on Twitter [7]
Sentiment analysis using
feed forward neural
network.
Reducing input sequence by
removing &, @.
Tweets from
Michigan’s
sentiment analysis.
Twitter API
Accuracy
achieved is
74.15%.
7 Sentiment Analysis on Twitter
using streaming API [8]
NLTK for tokenization and
convert unstructured data to
structural data.
Uses Naive Bayes for
Classification.
Twitter API It performs
analysis on
real time
data.
8 Product based data sentiment
content analysis based on
Convolution Neural Network
for the Chinese micro-blog [9]
Sentiment analysis using
Convolution Neural
Network
Chinese Micro blog
database
Better
accuracy
than lexicon
analysis.
9 A feature based approach for
sentiment analysis by using
Support Vector Machine
(SVM) [10]
Support Vector Machine for
classification from huge
data.
Reviews of product
from e-commerce
site amazon, eBay
Accuracy
increased
total of
88.13%
10 Localized based Opinion
mining using Sentiment
Analysis [11]
Rapidminer is used for
mining which extract
information using keywords.
Sentiword is used for
assigning polarities.
Twitter API Various
processes for
extraction of
data.
11 Aspect based feature
extraction & sentiment
classification of reviews data
sets using Incremental
Machine Learning algorithm
[12]
Identifies the sentiment,
aspect & performs data
classification.
It uses incremental decision
tree for classification.
Opinion summarization
using SVM & Naive Bayes.
General Data The result
shows that
SVM
method is
much better
than Bayes.
12 Sentiment Analysis on Social
Media using Morphological
Sentences Pattern model [1]
Extracts aspects &
expression using sentiment
pattern analyzer based on
MSP model.
Movie reviews
from IMDb
Rotten Tomatoes
Twitter
YouTube
Accuracy
increased to
91%
Increased by
2.2%
8. Volume 1, Issue 1 (2018)
Article No. 8
PP 1-8
8
www.viva-technology.org/New/IJRI
4. CONCLUSION
In this suggested approach, extensive study of numerous methods and practices used for sentiment
analysis are considered. Methods such as lexicon based approach, SVM, SentiReview, CNN, IML and
Morphological Sentence Pattern model are studied in this paper. Each method holds its own unique ability and
provides different results. Lexicon based approach and SVM are the methods used in the past, but with the
advancement in sentiment analysis various methods such as CNN and IML are being practiced more for better
result. Sentiment analysis plays a vital part in reviewing any product, system, etc. The methods stated in paper
have their advantages and disadvantages and can be used according to the system.
Acknowledgement
We would like to express a profound sense of gratitude towards Prof. Tatwadarshi P. N., Department of Computer Engineering for his
constant encouragement and valuable suggestions. The work that we have been able to present is possible because of his timely guidance
and support.
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