Abstract
Sentiment classification identifies the polarity of text such as positive, negative or neutral based on textual features. A tolerance near set-based text classifier (TSC) is introduced in this paper to classify sentiment polarities of text with vectors from a pre-trained SBERT algorithm. One of the datasets (Covid-Sentiment) was hand-crafted with tweets from Twitter of opinions related to COVID. Experiments demonstrate that TSC outperforms five classical ML algorithms with one dataset, and is comparable with all other datasets using a weighted F1-score.
Vrushang Patel’s work was supported by the UW President’s Distinguished Graduate Student Scholarship and Sheela Ramanna’s work was supported by NSERC Discovery Grant # 194376.
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Patel, V., Ramanna, S. (2021). Tolerance-Based Short Text Sentiment Classifier. In: Ramanna, S., Cornelis, C., Ciucci, D. (eds) Rough Sets. IJCRS 2021. Lecture Notes in Computer Science(), vol 12872. Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_22
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