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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). We evaluate the model using small,widely used Sentiment and subjectivity cor-pora and find it out-performs several previ-ously introduced methods for Sentiment clas-sification.]

ratings). We evaluate the model using small, ... model shares its probabilistic foundation with LDA, but is factored in a manner designed to discover ... based approaches are extremely successful in prac-tice, but they force the researcher to make a number of design choices (weighting, normalization, dimen- ...

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  Based, Analysis, Foundations, Model, Learning, Words, Ratings, Vector, Sentiment, Learning word vectors for sentiment analysis

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