A Novel Framework for Aspect Knowledgebase Generated Automatically from Social Media Using Pattern Rules

Authors

  • Tuan Anh Tran Prince of Songkla University
  • Jarunee Duangsuwan Prince of Songkla University
  • Wiphada Wettayaprasit Prince of Songkla University https://orcid.org/0000-0002-8233-0323

DOI:

https://doi.org/10.7494/csci.2021.22.4.4028

Keywords:

opinion mining, aspect knowledgebase, aspect extraction, pattern rules, social media

Abstract

One of the factors improving businesses in business intelligence is summarization systems which could generate summaries based on sentiment from social media. However, these systems could not produce automatically, they used annotated datasets. To automatically produce sentiment summaries without using the annotated datasets, we propose a novel framework using pattern rules. The framework has two procedures: 1) pre-processing and 2) aspect knowledgebase generation. The first procedure is to check and correct misspelt words (bigram and unigram) by a proposed method, and tag part-of-speech all words. The second procedure is to automatically generate aspect knowledgebase used to produce sentiment summaries by the sentiment summarization systems. Pattern rules and semantic similarity-based pruning are used to automatically generate aspect knowledgebase from social media. In the experiments, eight domains from benchmark datasets of reviews are used. The performance evaluation of our proposed approach shows the high performance when compared to other approaches.

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Published

2021-11-23

How to Cite

Tran, T. A., Duangsuwan, J., & Wettayaprasit, W. (2021). A Novel Framework for Aspect Knowledgebase Generated Automatically from Social Media Using Pattern Rules. Computer Science, 22(4). https://doi.org/10.7494/csci.2021.22.4.4028

Issue

Section

Articles