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Efficient Filtering of Noise Reviews Using Supervised Learning in Social Big Data Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(6), pp.63-69
  • DOI : 10.9708/jksci.2023.28.06.063
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 19, 2023
  • Accepted : June 8, 2023
  • Published : June 30, 2023

Hyeon Gyu Kim 1

1삼육대학교

Accredited

ABSTRACT

Social reviews collected through the search API may include a large number of reviews unrelated to a given search term, and these reviews are referred to as noise reviews because they may lead to distorted analysis results. In this paper, we discuss supervised learning algorithms to conduct filtering of the noise reviews efficiently, and compare their performance through experiments. About 20,000 reviews collected for tourist attractions in the Ulsan metropolitan city were used for the experiments, and LSTM and BERT, which are known to provide high accuracy in text processing, were adopted for training and testing the reviews. As a result, BERT provided better accuracy than LSTM, where f1-scores of the two algorithms were 90.1% and 95.2%, respectively. On the other hand, in terms of execution time, LSTM was about 5 times faster than BERT. The result shows that, in the noise review filtering, BERT can be used more properly when accuracy is important, whereas LSTM can be used more properly when performance is important or computation resources are insufficient.

Citation status

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