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).]

Latent Dirichlet Allocation (LDA; (Blei et al., 2003)) is a probabilistic document model that as-sumes each document is a mixture of latent top-ics. For each latent topic T, the model learns a conditional distribution p(wjT) for the probability that word w occurs in T. One can obtain a k-dimensional vector representation of words by first

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  Analysis, Learning, Talent, Words, Vector, Sentiment, Learning word vectors for sentiment analysis

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