This paper presents a generative model based on the language modeling approach for sentiment analysis. By characterizing the semantic orientation of documents as “favorable” (positive) or “unfavorable” (negative), this method captures the subtle information needed in text retrieval. In order to conduct this research, a language model based method is proposed to keep the dependent link between a “term” and
other ordinary words in the context of a triggered language model: first, a batch of terms in a domain are identified; second, two different language models representing classifying knowledge for every term are built up from subjective sentences; last, a classifying function based on the generation of a test document is defined for the sentiment analysis. When compared with Support Vector Machine, a popular discriminative model, the language modeling approach performs better on a Chinese digital product review corpus by a 3-fold cross-validation. This result
motivates one to consider finding more suitable language models for sentiment detection in future research.