Publication Type |
Journal Article |
School or College |
College of Engineering |
Department |
Computing, School of |
Creator |
Riloff, Ellen M. |
Other Author |
Patwardhan, Siddharth; Wiebe, Janyce |
Title |
Feature subsumption for opinion analysis |
Date |
2006 |
Description |
Lexical features are key to many approaches to sentiment analysis and opinion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexicosyntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and performance. We use the subsumption hierarchy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing unnecessary features. We show that reducing the feature set improves performance on three opinion classification tasks, especially when combined with traditional feature selection. |
Type |
Text |
Publisher |
Association for Computational Linguistics |
First Page |
1 |
Last Page |
9 |
Subject |
Feature subsumption; Sentiment analysis; Opinion detection; Subsumption hierarchy |
Subject LCSH |
Information retrieval |
Language |
eng |
Bibliographic Citation |
Riloff, E. M., Patwardhan, S., & Wiebe, J. (2006). Feature subsumption for opinion analysis. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP-06), 1-9. |
Rights Management |
(c) Riloff, E. M., Patwardhan, S., & Wiebe, J. |
Format Medium |
application/pdf |
Format Extent |
176,862 bytes |
Identifier |
ir-main,12430 |
ARK |
ark:/87278/s6c82tc9 |
Setname |
ir_uspace |
ID |
702433 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6c82tc9 |