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Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

Author

Listed:
  • Asad Masood Khattak
  • Rabia Batool
  • Fahad Ahmed Satti
  • Jamil Hussain
  • Wajahat Ali Khan
  • Adil Mehmood Khan
  • Bashir Hayat

Abstract

Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.

Suggested Citation

  • Asad Masood Khattak & Rabia Batool & Fahad Ahmed Satti & Jamil Hussain & Wajahat Ali Khan & Adil Mehmood Khan & Bashir Hayat, 2020. "Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation," Complexity, Hindawi, vol. 2020, pages 1-11, December.
  • Handle: RePEc:hin:complx:8892552
    DOI: 10.1155/2020/8892552
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    Cited by:

    1. Kitti Nagy & Jozef Kapusta, 2021. "Improving fake news classification using dependency grammar," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.
    2. Chuhan Wu & Fangzhao Wu & Tao Qi & Wei-Qiang Zhang & Xing Xie & Yongfeng Huang, 2022. "Removing AI’s sentiment manipulation of personalized news delivery," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.

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