Automatic construction of domain and aspect specific sentiment lexicons for customer review mining

• Authors: Juergen Bross;Heiko Ehrig
• Venue: Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
• Year: 2013

Automatically analyzing the opinions expressed in customer reviews is of high relevance in many application scenarios, e.g., market research, trend analysis, or reputation management. A great share of current sentiment analysis approaches makes use of special purpose lexicons that provide information about the polarity (e.g., positive or negative) of individual words and phrases. One major challenge is that the actual sentiment polarity of a specific expression is often context dependent (e.g., "long+ battery life" vs. "long- flash recycle time"). However, the vast majority of existing approaches focuses on creating general purpose lexicons. Especially in the context of mining customer review data, the use of such lexicons is rather suboptimal as they fail to adequately reflect the domain specific lexical usage. We propose a novel method that allows to automatically adapt and extend existing lexicons to a specific product domain. We follow a corpus-based approach and exploit the fact that many customer reviews exhibit some form of semi-structure. The method is fully automatic and thus scales well across different product domains. Our experiments show that the extracted lexicons are highly accurate and significantly improve the performance in a sentiment classification scenario.