INSIGHT Galway: Syntactic and Lexical Features for Aspect Based Sentiment Analysis

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 346–350, Dublin, Ireland, August 23-24, 2014. INSIGHT Galway: Syntactic and Lexical Features for Aspect Based Sentiment Analysis Sapna Negi Insight Centre for Data Analytics National University of Ireland Galway {sapna.negi, paul.buitelaar}@insight-centre.org Paul Buitelaar Insight Centre for Data Analytics National University of Ireland Galway Abstract This work analyses various syntactic and lexical features for sentence level aspect based sentiment analysis. The task fo- cuses on detection of a writer’s sentiment towards an aspect which is explicitly men- tioned in a sentence. The target sentiment polarities are positive, negative, conflict and neutral. We use a supervised learning approach, evaluate various features and report accuracies which are much higher than the provided baselines. Best features include unigrams, clauses, dependency re- lations and SentiWordNet polarity scores. 1 Introduction The term aspect refers to the features or aspects of a product, service or topic being discussed in a text. The task of detection of sentiment towards these aspects involves two major processing steps, identifying the aspects in the text and identifying the sentiments towards these aspects. Our work describes a submitted system in the Aspect Based Sentiment Analysis task of SemEval 2014 (Pontiki et al., 2014). The task was further divided into 4 subtasks; our work corresponds to the subtask 2, called Aspect Term Polarity Detection. We pre- dict the polarity of sentiments expressed towards the aspect terms which are already annotated in a sentence. The target polarity types are positive, negative, neutral and conflict. We employ a statistical classifier and experiment with various syntactic and lexical features. Se- lected features for the submitted system include words which hold certain dependency relations with the aspect terms, clause in which the aspect This work is licensed under a Creative Commons Attribu- tion 4.0 International Licence. Page numbers and proceed- ings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ term appears, unigrams, and sum of lexicon based sentiment polarities of the words in the clause. 2 Related Work Pang et al. (2002) proved that unigrams and bigrams, adjectives and part of speech tags are important features for a machine learning based sentiment classifier. Later, verbs and adjectives were also identified as important features (Ches- ley, 2006). Meena and Prabhakar (2007) per- formed sentence level sentiment analysis using rules based on clauses of a sentence. However, in our case we cannot simply consider the adjec- tives and verbs as features, since they might re- late to different aspects. For example, in the sen- tence ‘The pizza is the best if you like thin crusted pizza.’, sentiment towards ‘pizza’ is positive be- cause of the adjective ‘best’; however for the term ‘thin crusted pizza’, ‘like’ would be the sentiment verb. Therefore, only those adjectives and verbs which relate to the target aspect, can be consid- ered as the indicator of their polarity. Wilson et al. (2009) showed that the words which share cer- tain dependency relations with aspect terms, tend to indicate the sentiments expressed towards …

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