Paper: Learning Word Vectors for Sentiment Analysis

ACL ID P11-1015
Title Learning Word Vectors for Sentiment Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

Unsupervised vector-based approaches to se- mantics can model rich lexical meanings, but they largely fail to capture sentiment informa- tion that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsuper- vised and supervised techniques to learn word vectors capturing semantic term–document in- formation as well as rich sentiment content. The 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 the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity cor- pora and find it out-performs sever...