Implicit feature identification for opinion mining

Lazhar, Farek

doi:10.1504/ijbis.2019.10018102

Cited by 1 publication

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“…To get relevant information by retrieving and mining opinions from the huge volume is one of the tasks in opinion mining. Because of the beneficial effects on understanding customer opinions, opinion mining has been studied by researchers in many languages [1,8,12,21,22,25,29,32] and applied to sentiment-summarization systems [11,13,14,48]. These summarization systems can help people observe different perspectives from their summaries to make decisions.…”

Section: Introductionmentioning

confidence: 99%

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

Tran

Duangsuwan

Wettayaprasit

2021

csci

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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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