Paper: Compositional Matrix-Space Models for Sentiment Analysis

ACL ID D11-1016
Title Compositional Matrix-Space Models for Sentiment Analysis
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accu- rate assignment of phrase-level sentiment. For example, combining an adverb (e.g., “very”) with a positive polar adjective (e.g., “good”) produces a phrase (“very good”) with in- creased polarity over the adjective alone. In- spired by recent work on distributional ap- proaches to compositionality, we model each word as a matrix and combine words us- ing iterated matrix multiplication, which al- lows for the modeling of both additive and multiplicative semantic effects. Although the multiplication-based matrix-space framework has been shown to be a theore...