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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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Authors and Affiliations
Univ. Manouba, ENSI, RIADI LR99ES26, Campus universitaire, 2010, Manouba, Tunisia
Manel BenSassi & Maher Abbes
CEDRIC, Conservatoire National des Arts et des Métiers (CNAM) PARIS, Rue Saint Martin, 75003, Paris, France
Faten Atigui
- Manel BenSassi
Search author on:PubMed Google Scholar
- Maher Abbes
Search author on:PubMed Google Scholar
- Faten Atigui
Search author on:PubMed Google Scholar
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Correspondence toManel BenSassi,Maher Abbes orFaten Atigui.
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Renmin University of China, Beijing, China
Feng Zhang
Victoria University, Footscray, VIC, Australia
Hua Wang
Qatar University, Doha, Qatar
Mahmoud Barhamgi
Swinburne University of Technology, Hawthorn, Australia
Lu Chen
Swinburne University of Technology, Hawthorn, Australia
Rui Zhou
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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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