Vector based sentiment and emotion analysis from text: A survey

Abstract

As a primary means of communication, texts are used to implicitly or explicitly reflect emotions. Emotion or sentiment detection from text has emerged as an important and expanding research area to more clearly understand the actual feelings of humans. Most of the word representation models, such as Word2Vec or GloVe, project the words in vector space such that if words have similar context, then their representations are also very similar. However, according to the recent studies, this approach limits the success of studies in areas such as emotion detection. For instance, love and happy are emotionally similar words, but they may have a lower similarity score than emotionally dissimilar word such as happy and sad which have high co-occurrence frequency, as they are in similar contexts. Recently, researchers propose some methods based on the addition of emotional or sentimental information to the original word vectors. These have improved the vector representation of words and achieved better results in emotion detection or classification tasks. In this survey, we analyze in detail such recent text-based studies in the literature. We summarize their methods used, emotion models, data sources, findings, and performances.