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Fuzzy Based Text Quality Assessment for Sentiment Analysis

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14306))

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Abstract

Practitioners have emphasized the importance of employing sentiment analysis techniques in decision-making. The data utilized in this process is typically gathered from social media, making it somewhat unreliable for decision-making. To address this issue, this study focuses on the Text Quality (TQ) aspect to capture the characteristics of Twitter data streams. Our objective is to develop an automated approach that assists the user in assessing the quality of textual data. This is accomplished through a fuzzified classifier, which automatically identifies ambiguous and unambiguous text at both the syntactic and semantic levels. We present a software tool that captures real-time and batch Twitter data streams. This tool calculates their TQ and presents the outcomes through diverse graphical depictions. It also empowers users to customize the weights allocated to individual quality dimensions and metrics used in computing the overall data quality of a tweet. This flexibility enables customization of weights according to different analysis contexts and user profiles. To demonstrate the usability and value of our contributions, we conducted a case study focusing on the Covid-19 vaccine. A preliminary analysis shows that by removing ambiguous text, the accuracy of the deployed algorithms enhances.

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Author information

Authors and Affiliations

  1. Univ. Manouba, ENSI, RIADI LR99ES26, Campus universitaire, 2010, Manouba, Tunisia

    Manel BenSassi & Maher Abbes

  2. CEDRIC, Conservatoire National des Arts et des Métiers (CNAM) PARIS, Rue Saint Martin, 75003, Paris, France

    Faten Atigui

Authors
  1. Manel BenSassi
  2. Maher Abbes
  3. Faten Atigui

Corresponding authors

Correspondence toManel BenSassi,Maher Abbes orFaten Atigui.

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Feng Zhang

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Qatar University, Doha, Qatar

    Mahmoud Barhamgi

  4. Swinburne University of Technology, Hawthorn, Australia

    Lu Chen

  5. Swinburne University of Technology, Hawthorn, Australia

    Rui Zhou

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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BenSassi, M., Abbes, M., Atigui, F. (2023). Fuzzy Based Text Quality Assessment for Sentiment Analysis. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_2

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Chapter
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eBook
JPY 14871
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
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Softcover Book
JPY 18589
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only


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