TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification

Zhifei Zhang,Hongling Wang,Jian-Yun Nie

doi:10.18653/v1/s15-2131

Abstract

This paper describes the system we submitted to In-domain ABSA subtask of SemEval 2015 shared task on aspect-based sentiment analysis that includes aspect category detection and sentiment polarity classification. For the aspect category detection, we combined an SVM classifier with implicit aspect indicators. For the sentiment polarity classification, we combined an SVM classifier with a lexicon-based polarity classifier. Our system outperforms the baselines on both the laptop and restaurant domains and ranks above average on the laptop domain.

Highlights

Sentiment analysis aims at identifying people’s opinions, sentiments, attitudes, and emotions towards entities and their attributes (Liu, 2012), which has a wide range of applications on user-generated content, e.g., reviews, blogs, and tweets.Most previous work in sentiment analysis mainly attempted to identify the overall polarity of a given text or text span (Pang and Lee, 2008; Wilson et al, 2009; Zhang et al, 2009)
Our combination is done as follows: if the “General” category is suggested by the SVM classifier, we replace it by the categories identified through the implicit aspect indicators
There are 22 entity labels and 9 attribute labels on the laptop domain, and there are 6 entity labels and 5 attribute labels on the restaurant domain

Summary

Introduction

Sentiment analysis aims at identifying people’s opinions, sentiments, attitudes, and emotions towards entities and their attributes (Liu, 2012), which has a wide range of applications on user-generated content, e.g., reviews, blogs, and tweets. We need to discover the aspects of entities and determine the sentiment polarity on each entity aspect This task is called aspect-based sentiment analysis or featurebased opinion mining (Hu and Liu, 2004). The ABSA task consists of two subtasks: In-domain ABSA and Out-domain ABSA We participated in the former subtask that aims to identify the aspect category (i.e., an entity and attribute pair) and the sentiment polarity given a review text about a laptop or a restaurant. For the aspect category detection, an SVM classifier with the bagof-words features can be used, and this approach is used as our baseline method. For the sentiment polarity classification, an SVM classifier with the bag-of-words features plus the category feature is trained and this is used as our baseline. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 772–777, Denver, Colorado, June 4-5, 2015. c 2015 Association for Computational Linguistics

Aspect Category Detection

Implicit Aspect Indicator

SVM Classifier

Combination Classifier

Sentiment Polarity Classification

Lexicon-Based Polarity Classifier

Experimental Results

Conclusions