Sentiment analysis on UTHM issues with big data
Nowadays, social media platform such as Twitter, WhatsApp, Facebook and it Messenger, as well as Instagram plays a very importance role to the society. Twitter is a micro-blogging platform that is able to provide a remarkable amount of data that can be used in several number of sentiment analysis ap...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
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Penerbit UTHM
2020
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| Online Access: | http://eprints.uthm.edu.my/6228/1/AJ%202020%20%28246%29.pdf http://eprints.uthm.edu.my/6228/ https://doi.org/10.30880/jeva.2020.01.01.003 |
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| author | Suhaimi, Noor Suhaida Mahamad, Abd Kadir Saon, Sharifah Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Elmunsyah, Hakkun |
| author_facet | Suhaimi, Noor Suhaida Mahamad, Abd Kadir Saon, Sharifah Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Elmunsyah, Hakkun |
| author_sort | Suhaimi, Noor Suhaida |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Nowadays, social media platform such as Twitter, WhatsApp, Facebook and it Messenger, as well as Instagram plays a very importance role to the society. Twitter is a micro-blogging platform that is able to provide a remarkable amount of data that can be used in several number of sentiment analysis applications such as predictions, reviews, and elections. Sentiment Analysis is a process of extracting information of issues or specific topic from enormous amount of data and categorizes it into different classes. The main target of this project is to classify Twitter data into sentiments value either positive, neutral or negative on data collected regarding Universiti Tun Hussein Onn Malaysia (UTHM) issues. This sentiment was classified using sentiment classifier, while data is trained on a Naïve Bayes Classifier, on TextBlob Python library. Lastly, results were displayed to the user, through a web application using Jupyter Notebook. This study found out that the percentage for positive, neutral and negative tweets regarding UTHM issues were 74%, 26% and 0% in English tweets, meanwhile 17%, 82% and 1 % of Bahasa Melayu tweets, respectively. Positive and neutral sentiments analysis shows positive perception of the products and services, thus promoting and branding UTHM worldwide. |
| format | Article |
| id | my.uthm.eprints-6228 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2020 |
| publisher | Penerbit UTHM |
| record_format | eprints |
| spelling | my.uthm.eprints-62282022-01-27T05:51:05Z http://eprints.uthm.edu.my/6228/ Sentiment analysis on UTHM issues with big data Suhaimi, Noor Suhaida Mahamad, Abd Kadir Saon, Sharifah Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Elmunsyah, Hakkun TA168 Systems engineering Nowadays, social media platform such as Twitter, WhatsApp, Facebook and it Messenger, as well as Instagram plays a very importance role to the society. Twitter is a micro-blogging platform that is able to provide a remarkable amount of data that can be used in several number of sentiment analysis applications such as predictions, reviews, and elections. Sentiment Analysis is a process of extracting information of issues or specific topic from enormous amount of data and categorizes it into different classes. The main target of this project is to classify Twitter data into sentiments value either positive, neutral or negative on data collected regarding Universiti Tun Hussein Onn Malaysia (UTHM) issues. This sentiment was classified using sentiment classifier, while data is trained on a Naïve Bayes Classifier, on TextBlob Python library. Lastly, results were displayed to the user, through a web application using Jupyter Notebook. This study found out that the percentage for positive, neutral and negative tweets regarding UTHM issues were 74%, 26% and 0% in English tweets, meanwhile 17%, 82% and 1 % of Bahasa Melayu tweets, respectively. Positive and neutral sentiments analysis shows positive perception of the products and services, thus promoting and branding UTHM worldwide. Penerbit UTHM 2020 Article PeerReviewed text en http://eprints.uthm.edu.my/6228/1/AJ%202020%20%28246%29.pdf Suhaimi, Noor Suhaida and Mahamad, Abd Kadir and Saon, Sharifah and Ahmadon, Mohd Anuaruddin and Yamaguchi, Shingo and Elmunsyah, Hakkun (2020) Sentiment analysis on UTHM issues with big data. Journal of Electronics Voltage and Application, 1 (1). pp. 20-26. ISSN 27166074 https://doi.org/10.30880/jeva.2020.01.01.003 |
| spellingShingle | TA168 Systems engineering Suhaimi, Noor Suhaida Mahamad, Abd Kadir Saon, Sharifah Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Elmunsyah, Hakkun Sentiment analysis on UTHM issues with big data |
| title | Sentiment analysis on UTHM issues with big data |
| title_full | Sentiment analysis on UTHM issues with big data |
| title_fullStr | Sentiment analysis on UTHM issues with big data |
| title_full_unstemmed | Sentiment analysis on UTHM issues with big data |
| title_short | Sentiment analysis on UTHM issues with big data |
| title_sort | sentiment analysis on uthm issues with big data |
| topic | TA168 Systems engineering |
| url | http://eprints.uthm.edu.my/6228/1/AJ%202020%20%28246%29.pdf http://eprints.uthm.edu.my/6228/ https://doi.org/10.30880/jeva.2020.01.01.003 |
| url_provider | http://eprints.uthm.edu.my/ |