Aspect Based Opinion Summarization Using Rule-Based Method and Support Vector Machine for Indonesian Reviews

Pindya Khabiibah, Sari Widya Sihwi, Haryono Setiadi


E-commerce's reviews feature can help users find information about the desired skin care products to choose the right one. However, the reviews number of a product grows rapidly due to the popularity of e-commerce and the product itself. A user becomes difficult to read all reviews one by one and extract useful information. To deal with this problem, we summarize aspects using the Rule-Based method and Support Vector Machine. We propose a Rule-Based method that is used to break down a review into several segments based on its aspect. Support Vector Machine is used to classify sentence segments according to their polarity. The data used in this study is Indonesian reviews of skin care products obtained from the Female Daily website. The average accuracy results using 10-fold cross-validation of sentiment classification is 74%. We experimented on 462 reviews where the accuracy is 92% in aspect categorization and 71.2% in sentiment classification. Based on humans, the lowest value is the suitability of a sentence with its sentiment/polarity. The highest value is the suitability of sentences with its aspect and usability of summary to helps users to find specific information so they can decide whether to buy that product or not. It can be concluded that the reader can well receive the summary. Future work can consider the negation word to reduce misclassification in the sentiment classification step.


Opinion summarization; product review; rule-based; support vector machine.

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