Transcription of Learning Word Vectors for Sentiment Analysis
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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).]
dimensional vector representation of words by first training a k-topic model and then filling the matrix with the p(wjT) values (normalized to unit length). The result is a word–topic matrix in which the rows are taken to represent word meanings. However, because the emphasis in LDA is …
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