Sentiment Analysis using Twitter Dataset
Apparently Social media sites are becoming increasingly popular, it creates platforms through which organizations, communities, and individuals share and discuss various topics. The reviews and data obtained from these sites are essential for further analysis. In this paper we studied the sentiment...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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State Islamic University Sunan Kalijaga
2020-10-01
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| Series: | IJID (International Journal on Informatics for Development) |
| Subjects: | |
| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/1790 |
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| _version_ | 1850149780039139328 |
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| author | Ibrahim Moge Noor Metin Turan |
| author_facet | Ibrahim Moge Noor Metin Turan |
| author_sort | Ibrahim Moge Noor |
| collection | DOAJ |
| description | Apparently Social media sites are becoming increasingly popular, it creates platforms through which organizations, communities, and individuals share and discuss various topics. The reviews and data obtained from these sites are essential for further analysis. In this paper we studied the sentiment classification of 2019 Kenyan 1000 banknote demonetization using Twitter as our source dataset. We perform Multi nominal naïve Bayes classifier algorithm to classify tweets documents. We split our dataset using k-folder validation since we had limited amounts of data, so to achieve unbiased prediction of the model. . We obtained in test data an accuracy of 70.8% when we used unigram model and 64.1% when we applied bigram model. Results show that the model reached to an acceptable accuracy of (71%) on average using unigram model. |
| format | Article |
| id | doaj-art-6fce7ab3897d4ac4944d83f621319e2d |
| institution | OA Journals |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2020-10-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-6fce7ab3897d4ac4944d83f621319e2d2025-08-20T02:26:47ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482020-10-0182849410.14421/ijid.2019.08206658Sentiment Analysis using Twitter DatasetIbrahim Moge Noor0Metin Turan1Department of Engineering, Faculty of Computer Engineering Istanbul Commerce University IstanbulDepartment of Engineering, Faculty of Computer Engineering Istanbul Commerce University IstanbulApparently Social media sites are becoming increasingly popular, it creates platforms through which organizations, communities, and individuals share and discuss various topics. The reviews and data obtained from these sites are essential for further analysis. In this paper we studied the sentiment classification of 2019 Kenyan 1000 banknote demonetization using Twitter as our source dataset. We perform Multi nominal naïve Bayes classifier algorithm to classify tweets documents. We split our dataset using k-folder validation since we had limited amounts of data, so to achieve unbiased prediction of the model. . We obtained in test data an accuracy of 70.8% when we used unigram model and 64.1% when we applied bigram model. Results show that the model reached to an acceptable accuracy of (71%) on average using unigram model.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/1790machine learningmultinomial naïve bayessentiment analysistwitter datan-gram |
| spellingShingle | Ibrahim Moge Noor Metin Turan Sentiment Analysis using Twitter Dataset IJID (International Journal on Informatics for Development) machine learning multinomial naïve bayes sentiment analysis twitter data n-gram |
| title | Sentiment Analysis using Twitter Dataset |
| title_full | Sentiment Analysis using Twitter Dataset |
| title_fullStr | Sentiment Analysis using Twitter Dataset |
| title_full_unstemmed | Sentiment Analysis using Twitter Dataset |
| title_short | Sentiment Analysis using Twitter Dataset |
| title_sort | sentiment analysis using twitter dataset |
| topic | machine learning multinomial naïve bayes sentiment analysis twitter data n-gram |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/1790 |
| work_keys_str_mv | AT ibrahimmogenoor sentimentanalysisusingtwitterdataset AT metinturan sentimentanalysisusingtwitterdataset |