Paper: ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification

ACL ID S14-2041
Title ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper reports our submissions to the four subtasks of Aspect Based Sentimen- t Analysis (ABSA) task (i.e., task 4) in SemEval 2014 including aspect term ex- traction and aspect sentiment polarity clas- sification (Aspect-level tasks), aspect cat- egory detection and aspect category sen- timent polarity classification (Category- level tasks). For aspect term extraction, we present three methods, i.e., noun phrase (NP) extraction, Named Entity Recogni- tion (NER) and a combination of NP and NER method. For aspect sentiment classi- fication, we extracted several features, i.e., topic features, sentiment lexicon features, and adopted a Maximum Entropy classifi- er. Our submissions rank above average.