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

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 772–777, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification Zhifei Zhang and Jian-Yun Nie Dept. of Comp. Sci. and Oper. Res. University of Montreal Quebec H3C 3J7, Canada {zhanzhif, nie}@iro.umontreal.ca Hongling Wang Dept. of Comp. Sci. and Tech. Soochow University Suzhou 215006, China hlwang@suda.edu.cn Abstract This paper describes the system we submitted to In-domain ABSA subtask of SemEval 2015 shared task on aspect-based sentiment analy- sis that includes aspect category detection and sentiment polarity classification. For the as- pect category detection, we combined an SVM classifier with implicit aspect indicators. For the sentiment polarity classification, we com- bined an SVM classifier with a lexicon-based polarity classifier. Our system outperforms the baselines on both the laptop and restaurant do- mains and ranks above average on the laptop domain. 1 Introduction Sentiment analysis aims at identifying people’s opinions, sentiments, attitudes, and emotions to- wards 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 giv- en text or text span (Pang and Lee, 2008; Wilson et al., 2009; Zhang et al., 2009). The document-level or sentence-level sentiment classification is often in- sufficient for applications. Each document may talk about different entities, or express different opinions about different aspects of the entity even if the doc- ument concerns a single entity. Therefore, 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 feature- based opinion mining (Hu and Liu, 2004). The aspect-based sentiment analysis (ABSA) task (Task 12) (Pontiki et al., 2015) in SemEval 2015 is a continuation of SemEval 2014 Task 4 (Pontiki et al., 2014). The ABSA task consists of two subtasks: In-domain ABSA and Out-domain ABSA. We par- ticipated 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. Each entity and attribute pair is an aspect catego- ry chosen from the predefined inventories of entity types and attribute labels per domain. For the aspect category detection, an SVM classifier with the bag- of-words features can be used, and this approach is used as our baseline method. However, if a token implying an aspect, e.g., “overpriced”, is not tak- en as a feature, the SVM classifier cannot correct- ly identify its corresponding category. Therefore, we enhance the results from the SVM classifier by using implicit aspect indicators (Cruz-Garcia et al., 2014). 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. However, again, if a sentiment word does not appear in the training data, the SVM classifier …

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