An LSTM Approach to Short Text Sentiment Classification with Word Embeddings

An LSTM Approach to Short Text Sentiment Classification with Word Embeddings

Sentiment classification techniques have been widely used for analyzing user opinions. In conventional supervised learning methods, hand-crafted features are needed, which requires a thorough understanding of the domain. Since social media posts are usually very short, there's a lack of features for effective classification. Thus, word embedding models can be used to learn different word usages in various contexts. To detect the sentiment polarity from short texts, we need to explore deeper semantics of words using deep learning methods. In this paper, we investigate the effects of word embedding and long short-term memory (LSTM) for sentiment classification in social media. First, words in posts are converted into vectors using word embedding models. Then, the word sequence in sentences are input to LSTM to learn the long distance contextual dependency among words. The experimental results showed that deep learning methods can effectively learn the word usage in context of social media given enough training data. The quantity and quality of training data greatly affects the performance. Further investigation is needed to verify the performance in different social media sources.