Abstract
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 \(\beta \) 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.
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Acknowledgements
This work was supported by the Russian Science Foundation grant no. 15-11-10019. We thank Alexander Panchenko and Nikolay Arefyev for providing us the word2vec model and its Russian-language training data.
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Tutubalina, E., Nikolenko, S. (2017). Constructing Aspect-Based Sentiment Lexicons with Topic Modeling. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_20
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