Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis

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dc.contributor.authorAli Alrababah, Saif Addeen Ahmad
dc.date.accessioned2019-03-06T08:15:50Z
dc.date.available2019-03-06T08:15:50Z
dc.date.issued2018-04
dc.description.abstractIdentifying product aspects in customer reviews can have a great influence on both business strategies as well as on customers’ decisions. Presently, most research focuses on machine learning, statistical, and Natural Language Processing (NLP) techniques to identify the product aspects in customer reviews. The challenge of this research is to formulate aspect identification as a decision-making problem. To this end, we propose a product aspect identification approach by combining multi-criteria decision-making (MCDM) with sentiment analysis. The suggested approach consists of two stages namely product aspect extraction and product aspect ranking. For product aspect extraction stage, an unsupervised approach is proposed for identifying explicit opinionated aspects and attributes that are strongly related to a specific domain product mathematically using lexicographer files in WordNet. The empirical evaluation of the product aspect extraction approach using online reviews of two popular datasets of supervised and unsupervised systems in terms of recall, precision, and F-measure, showed that our approach achieved competitive results for aspect extraction from product reviews, especially for precision measure.For product aspect ranking stage, two approaches have been proposed to rank the extracted aspects, as these aspects differ in their significances, namely Subjective TOPSIS and IPSI-TOPSIS. These two approaches ranked the aspects based on three extraction criteria jointly: frequency-based, opinion-based, and aspect relevancy. The IPSI-TOPSIS approach is distinguished by the objective criteria weighting technique instead of subjective weighting which is used in Subjective TOPSIS. For the evaluation process, the proposed ranking methods are compared against different baseline approaches. The comparison results using the NDCG ranking measure revealed that the two proposed ranking methods outperform the baseline approaches in prioritising the genuine product aspects in customer feedback.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7849
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectMulti criteria decision making approachen_US
dc.subjectaspect-based sentiment analysisen_US
dc.titleMulti Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysisen_US
dc.typeThesisen_US
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