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Constructing aspect-based sentiment lexicons with topic modeling
We study topic models designed to be used for sentiment analysis, i.e., models that extract certain topics (aspects) from a corpus of documents and mine sentiment-related labels related to individual aspects. For both direct applications in sentiment analysis and other uses, it is desirable to have a good lexicon of sentiment words, preferably related to different aspects in the words. We have previously developed a modification for several popular sentiment-related LDA extensions that trains prior hyperparameters β for specific words. We continue this work and show how this approach leads to new aspect-specific lexicons of sentiment words based on a small set of “seed” sentiment words; the lexicons are useful by themselves and lead to improved sentiment classification.