Improving the Accuracy of Sentiment Analysis of SNS Comments Using Transfer Learning and Its Application to Flaming Detection

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  • 転移学習を用いたSNSにおける感情分析の精度向上と炎上検知への応用
  • テンイ ガクシュウ オ モチイタ SNS ニ オケル カンジョウ ブンセキ ノ セイド コウジョウ ト エンジョウ ケンチ エ ノ オウヨウ

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In recent years, along with the popularization of SNS, the incidents, which are called flaming, that the number of negative comments surges are on the increase. This becomes a problem for companies because flamings hurt companies' reputation. In order to minimalize the damage of reputation, we propose the method that detects flamings by estimating the sentiment polarities of SNS comments. Because of the unique SNS characteristics such as repetition of same comments, the polarities of words are sometimes wrongly estimated. To alleviate this problem, transfer learning is introduced. In this research, the sentiment polarities of words are trained in every domain. This will enable to extract the words that are domain-specific and dictate the polarity of comments. These words are occurred in retweets. Transfer learning is implemented to non-extracted words by averaging the occurrence probabilities in other domains. These processes keep the polarities of important words that dictate the polarity of comments and modify the wrongly estimated polarities of words. The experimental results show that the proposed method improves the performance of estimating the sentiment polarity of comments. Moreover, flamings can be detected without missing by monitoring time course of the number of negative comments.

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