Paper: SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis

ACL ID S14-2092
Title SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes our SeemGo sys- tem for the task of Aspect Based Sen- timent Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that ob- tains the F-score of 0.683 for Laptops and 0.791 for Restaurants by exploiting both word-based features and context features. The other three subtasks are solved by the Maximum Entropy model, with the occur- rence counts of unigram and bigram words of each sentence as features. The sub- task of aspect category detection obtains the best result when applying the Boosting method on the Maximum Entropy model, with the precision of 0.869 for Restau- rants. The Maximum Entropy model also shows good performance in the subtasks of both aspect term and aspect category ...