SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis
Pengfei Liu, Helen M. L. Meng · 2014
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