Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents |
| |
| Authors: | Email author" target="_blank">Kiran?SarvabhotlaEmail author Prasad?Pingali Vasudeva?Varma |
| |
| Institution: | (1) Search and Information Extraction Lab, International Institute of Information Technology, Hyderabad, India |
| |
| Abstract: | With the growth of social media, document sentiment classification has become an active area of research in this decade. It
can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment).
Hence, the key task in document sentiment classification is extracting subjectivity. Existing approaches to extract subjectivity
rely heavily on linguistic resources such as sentiment lexicons and complex supervised patterns based on part-of-speech (POS)
information. This makes the task of subjective feature extraction complex and resource dependent. In this work, we try to
minimize the dependency on linguistic resources in sentiment classification. We propose a simple and statistical methodology
called review summary (RSUMM) and use it in combination with well-known feature selection methods to extract subjectivity.
Our experimental results on a movie review dataset prove the effectiveness of the proposed methodology. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
