A Language Modeling Approach to Sentiment Analysis

This paper presents a language modeling approach to the sentiment detection problem. It captures the subtle information in text processing to character the semantic orientation of documents as “thumb up” (positive) or “thumb down” (negative). To handle this problem, we propose an idea to estimate both the positive and negative language models from training collections. Tests are done through computing the Kullback-Leibler divergence between the language model estimated from test document and these two trained sentiment models. We assert the polarity of a test document by observing whether its language model is close to the trained “thumb up” model or the “thumb down” model. When compared with an outstanding classifier, i.e., SVMs on movie review corpus, language modeling approach showed its better performance.