Transcription of Learning Word Vectors for Sentiment Analysis
{{id}} {{{paragraph}}}
Example: bachelor of science
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.]
Learning Word Vectors for Sentiment Analysis Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts Stanford University
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: