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Learning Word Vectors for Sentiment Analysis

Learning Word Vectors for Sentiment AnalysisAndrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang,Andrew Y. Ng,andChristopher PottsStanford UniversityStanford, CA 94305[amaas, rdaly, ptpham, yuze, ang, vector -based approaches to se-mantics can model rich lexical meanings, butthey largely fail to capture Sentiment informa-tion that is central to many word meanings andimportant for a wide range of NLP tasks. Wepresent a model that uses a mix of unsuper-vised and supervised techniques to learn wordvectors capturing semantic term document in-formation as well as rich Sentiment proposed model can leverage both con-tinuous and multi-dimensional Sentiment in-formation as well as non- Sentiment annota-tions. We instantiate the model to utilize thedocument-level Sentiment polarity annotationspresent in many online documents ( starratings).]

work introduces extensions of LDA to capture sen-timent in addition to topical information (Li et al., 2010; Lin and He, 2009; Boyd-Graber and Resnik, 2010). Like LDA, these methods focus on model-ing sentiment-imbued topics rather than embedding words in a vector space. Vector space models (VSMs) seek to model words directly (Turney and Pantel ...

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