The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection

Minjun Kim, Hiroki Sayama

研究成果: Conference contribution

抄録

Text classification is one of the most critical areas in machine learning and artificial intelligence research. It has been actively adopted in many business applications such as conversational intelligence systems, news articles categorizations, sentiment analysis, emotion detection systems, and many other recommendation systems in our daily life. One of the problems in supervised text classification models is that the models’ performance depends heavily on the quality of data labeling that is typically done by humans. In this study, we propose a new network community detection-based approach to automatically label and classify text data into multiclass value spaces. Specifically, we build networks with sentences as the network nodes and pairwise cosine similarities between the Term Frequency-Inversed Document Frequency (TFIDF) vector representations of the sentences as the network link weights. We use the Louvain method to detect the communities in the sentence networks. We train and test the Support Vector Machine and the Random Forest models on both the human-labeled data and network community detection labeled data. Results showed that models with the data labeled by the network community detection outperformed the models with the human-labeled data by 2.68–3.75% of classification accuracy. Our method may help developments of more accurate conversational intelligence and other text classification systems.

本文言語English
ホスト出版物のタイトルProceedings of NetSci-X 2020
ホスト出版物のサブタイトル6th International Winter School and Conference on Network Science
編集者Naoki Masuda, Kwang-Il Goh, Tao Jia, Junichi Yamanoi, Hiroki Sayama
出版社Springer
ページ231-243
ページ数13
ISBN(印刷版)9783030389642
DOI
出版ステータスPublished - 2020
イベント6th International School and Conference on Network Science, NetSci-X 2020 - Tokyo, Japan
継続期間: 2020 1 202020 1 23

出版物シリーズ

名前Springer Proceedings in Complexity
ISSN(印刷版)2213-8684
ISSN(電子版)2213-8692

Conference

Conference6th International School and Conference on Network Science, NetSci-X 2020
CountryJapan
CityTokyo
Period20/1/2020/1/23

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Computer Science Applications

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