How do you incorporate domain knowledge and external features into sentiment analysis with RNNs?

1 What are RNNs and why are they useful for sentiment analysis?

RNNs are a type of neural network that can process sequential data, such as text, speech, or video. They have a memory that allows them to store and access information from previous inputs, which makes them suitable for modeling context and long-term dependencies. For sentiment analysis, RNNs can capture the meaning and sentiment of a text by reading it word by word and updating their hidden state accordingly.

2 How to build a basic RNN model for sentiment analysis?

In order to construct a basic RNN model for sentiment analysis, the following steps should be taken: preprocessing the text data by tokenizing, lowercasing, and removing punctuation and stopwords; converting the text tokens into numerical vectors using word embeddings such as Word2Vec or GloVe; defining the RNN architecture by selecting the type of RNN cell (e.g. LSTM or GRU), the number of layers, the hidden size, and the output size; training the RNN model on a labeled dataset of texts and sentiments, utilizing a loss function (such as binary cross-entropy) and an optimizer (e.g. Adam); and finally, evaluating the RNN model on a test or validation dataset, measuring metrics such as accuracy, precision, recall, or F1-score.

3 How to incorporate domain knowledge into sentiment analysis?

Domain knowledge is the specific information and expertise that relates to a particular domain or topic, such as finance, sports, or politics. Incorporating domain knowledge into sentiment analysis can be beneficial as it provides additional context and vocabulary that can influence the sentiment of a text. For example, the word "bullish" may have a positive connotation in finance but a negative one in animal welfare. There are several methods to incorporate domain knowledge into sentiment analysis, such as using pre-trained word embeddings trained on a relevant corpus of texts from your domain, domain-specific lexicons or dictionaries with predefined sentiment polarity or intensity, and domain adaptation techniques that transfer knowledge from a general or source domain to a specific or target domain.

4 How to incorporate external features into sentiment analysis?

External features, like the author, date, genre, audience, or tone, are additional information that can affect the sentiment of a text without being part of it. Incorporating external features into sentiment analysis can improve accuracy by providing more clues and signals. For example, the sentiment of a tweet may be affected by user profile, hashtags, mentions, or emojis. To do this, you can use feature engineering to encode the external features as numerical or categorical variables, feature concatenation to combine them with text features like word embeddings or RNN outputs before feeding them to the classifier, or feature fusion to integrate them with text features at different levels of the RNN architecture.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?