Domain-specific sentiment analysis using contextual feature generation

A novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features and bootstrap from a small set of seed clues and generate new clues by utilizing linguistic dependencies and collocation information between sentiment clues and sentiment topics.

Abstract

This paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more contextual-information such as topic or domain is essential. In our system, we first automatically extract sentiment clues in different domains by our observation. We identified that a sentiment clue is often syntactically related to a sentiment topic in a sentence, which is defined as a primary subject of sentiment expression, such as event, company, and person. We bootstrap from a small set of seed clues and generate new clues by utilizing linguistic dependencies and collocation information between sentiment clues and sentiment topics. Next, we learn a domain-specific sentiment classifier for each domain with the newly aggregated clues. We ran experiments to see how the bootstrapping algorithm to converge and aggregate new clues and verified that the extracted domain-context features are more effective than generally-used features in sentiment analysis by running them on the same sentiment classifier.