Determination of text polarity classification using sentiment analysis
To effectively, analyzing the unstructured data or text for reviewing purposes need a robust tool to process and represent the result in comprehensive data visualization. Texts reviews as responded from thousands of reviewers, customers’ experiences or reputations and comments from the netizens are...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Unit Penerbitan JSKM
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/132174/1/132174.pdf https://ir.uitm.edu.my/id/eprint/132174/ https://appspenang.uitm.edu.my/sigcs/ |
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| Summary: | To effectively, analyzing the unstructured data or text for reviewing purposes need a robust tool to process and represent the result in comprehensive data visualization. Texts reviews as responded from thousands of reviewers, customers’ experiences or reputations and comments from the netizens are classified accordingly to derive overall perceptions on certain issues, products or views. The text reviews from the social media such as Facebook, twitter, Instagram or WhatsApp are the best platform to retrieve the specific field to perform the polarity classification. Polarity can be expressed as the numerical rating or sentiment score conveyed by a particular text, phrase or word. This paper will discuss phases that involves in Sentiment Analysis (SA) such as the Data Extraction, Data Pre-processing, Data Annotation, Polarity Detection, Evaluation and finally the Data Visualization. Two methods for data classification such as Machine Learning and Lexicon-based approaches have been employed to train the machine or tools to learn the data. Samples of python codes were provided at each phase of SA processes to demonstrate the classification and perform the data visualization based on the text reviews. |
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