Extracting Potentially High Profit Product Feature Groups by Using High Utility Pattern Mining and Aspect Based Sentiment Analysis

Demir, Seyfullah
Alkan, Öznur
Çekinel, Recep Fırat
Karagöz, Pınar
As a subproblem of sentiment analysis topic, aspect based sentiment analysis aims to extract distinct opinions for different aspects of a case in a given text. When the case is product review, it is possible to understand reviewer’s opinion on features of the product, rather than the product in general. Then, a product feature can be associated with a sentiment score denoting user satisfaction value by that feature. Modeling features mentioned in a review as items in a transaction may provide better insight into questions such as how to market products more effectively through analyzing properties that are more preferred to exist together in products. Sentiments behind feature groups enable decision makers further to understand and rank the feature groups that can lead to better marketing decisions, which constitutes the main motivation behind our work. In this paper, we propose a method that combines high utility pattern mining and aspect based sentiment analysis in order to extract groups of features that potentially increase profit and that need to be improved in order to increase user satisfaction. Experiments performed on patterns extracted by the proposed approach in comparison to the baselines show the potential to reveal high profit feature groups.


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Citation Formats
S. Demir, Ö. Alkan, R. F. Çekinel, and P. Karagöz, Extracting Potentially High Profit Product Feature Groups by Using High Utility Pattern Mining and Aspect Based Sentiment Analysis. 2019, p. 260.