Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier
In every product sold on the E-commerce platform, there is a review column from consumers who have made transactions on the products. These reviews are in the form of comments and ratings (stars from one to five) written and given by consumers based on their assessment of the products purchased. Wit...
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2021-08-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/721 |
| _version_ | 1797850435435888640 |
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| author | Rifki Kosasih Anggi Alberto |
| author_facet | Rifki Kosasih Anggi Alberto |
| author_sort | Rifki Kosasih |
| collection | DOAJ |
| description | In every product sold on the E-commerce platform, there is a review column from consumers who have made transactions on the products. These reviews are in the form of comments and ratings (stars from one to five) written and given by consumers based on their assessment of the products purchased. With the product evaluation feature based on the rating, the consumer can find out how good or bad the quality of the product is. However, a problem arises when some consumers give negative comments with five stars or vice versa. This causes the product assessment feature based on the rating to be less good so that it does not represent the real value. Therefore, to determine the quality of the product, sentiment analysis was carried out using the TF-IDF method and the Naive Bayes Classifier based on reviews from buyers. The data collected is 1000 reviews which are divided into 700 training data and 300 test data. The next stage is the preprocessing text such as case folding (converting uppercase letters to lowercase), tokenizing (separating sentences into single words), stopwords (removing tokenizing conjunctions that have nothing to do with sentiment analysis), stemming (changing words into basic word forms), and word weighting with TF-IDF. The last step is to classify. Based on the classification results obtained an accuracy rate of 80.2223%. |
| first_indexed | 2024-04-09T19:00:12Z |
| format | Article |
| id | doaj.art-624c4a86e2f942348b4ee1d64489229e |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-09T19:00:12Z |
| publishDate | 2021-08-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-624c4a86e2f942348b4ee1d64489229e2023-04-08T08:20:09ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792021-08-0113210110910.33096/ilkom.v13i2.721.101-109277Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifierRifki Kosasih0Anggi Alberto1Universitas GunadarmaUniversitas GunadarmaIn every product sold on the E-commerce platform, there is a review column from consumers who have made transactions on the products. These reviews are in the form of comments and ratings (stars from one to five) written and given by consumers based on their assessment of the products purchased. With the product evaluation feature based on the rating, the consumer can find out how good or bad the quality of the product is. However, a problem arises when some consumers give negative comments with five stars or vice versa. This causes the product assessment feature based on the rating to be less good so that it does not represent the real value. Therefore, to determine the quality of the product, sentiment analysis was carried out using the TF-IDF method and the Naive Bayes Classifier based on reviews from buyers. The data collected is 1000 reviews which are divided into 700 training data and 300 test data. The next stage is the preprocessing text such as case folding (converting uppercase letters to lowercase), tokenizing (separating sentences into single words), stopwords (removing tokenizing conjunctions that have nothing to do with sentiment analysis), stemming (changing words into basic word forms), and word weighting with TF-IDF. The last step is to classify. Based on the classification results obtained an accuracy rate of 80.2223%.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/721featureproductsentiment analysistf-idfnaive bayes classifier |
| spellingShingle | Rifki Kosasih Anggi Alberto Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier Ilkom Jurnal Ilmiah feature product sentiment analysis tf-idf naive bayes classifier |
| title | Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier |
| title_full | Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier |
| title_fullStr | Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier |
| title_full_unstemmed | Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier |
| title_short | Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier |
| title_sort | sentiment analysis of game product on shopee using the tf idf method and naive bayes classifier |
| topic | feature product sentiment analysis tf-idf naive bayes classifier |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/721 |
| work_keys_str_mv | AT rifkikosasih sentimentanalysisofgameproductonshopeeusingthetfidfmethodandnaivebayesclassifier AT anggialberto sentimentanalysisofgameproductonshopeeusingthetfidfmethodandnaivebayesclassifier |