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Author(s):  
S. Anita Shanthi ◽  
R. Valarmathi
Keyword(s):  

Author(s):  
S. Anita Shanthi ◽  
R. Valarmathi
Keyword(s):  

2021 ◽  
Author(s):  
◽  
Vrushang Patel

Text classification is a classical machine learning application in Natural Language Processing, which aims to assign labels to textual units such as documents, sentences, paragraphs, and queries. Applications of text classification include sentiment classification and news categorization. Sentiment classification identifies the polarity of text such as positive, negative or neutral based on textual features. In this thesis, we implemented a modified form of a tolerance-based algorithm (TSC) to classify sentiment polarities of tweets as well as news categories from text. The TSC algorithm is a supervised algorithm that was designed to perform short text classification with tolerance near sets (TNS). The proposed TSC algorithm uses pre-trained SBERT algorithm vectors for creating tolerance classes. The effectiveness of the TSC algorithm has been demonstrated by testing it on ten well-researched data sets. 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 measure.


Author(s):  
S. Anita Shanthi ◽  
R. Valarmathi ◽  
L. Darwin Christdhas Henry
Keyword(s):  

2021 ◽  
Vol 1724 (1) ◽  
pp. 012042
Author(s):  
S Anita Shanthi ◽  
R Valarmathi
Keyword(s):  

Author(s):  
Ghozzi Yosr ◽  
Nesrine Baklouti ◽  
Hani Hagras ◽  
Mounir Ben ayed ◽  
Adel M. Alimi

2020 ◽  
Vol 9 (11) ◽  
pp. 9713-9717
Author(s):  
S. Anita Shanthi ◽  
R. Valarmathi
Keyword(s):  

2020 ◽  
Vol 9 (4) ◽  
pp. 1521-1532
Author(s):  
S. Anita Shanthi ◽  
R. Valarmathi
Keyword(s):  

Author(s):  
Deivid de Almeida Padilha da Silva ◽  
Daniel Caio de Lima ◽  
José Hiroki Saito
Keyword(s):  

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