Santoso, Satriya Bagus (2022) SENTIMENT ANALYSIS OF PUBLIC FIGURE INSTAGRAM COMMENTS DATA USING NAIVE BAYES CLASSIFIER METHOD. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
ABSTRACT Instagram is a social media for sharing images, photos and videos accompanied by descriptions. Instagram has many active users from all walks of life, from general users, artists, public figures to senior officials, in uploading their posts, they must get hundreds to thousands of comments from Instagram social media users, whether it's a constructive criticism or a disapproval. Posts uploaded by Instagram users, especially public figures, have their own sentiments for the community, these sentiments are expressed through comments on posts in the form of positive and negative opinions or sentences that are determined manually by the reader. Sentiment analysis is needed for automatic classification of comments on Instagram, in this study sentiment analysis is in the form of a textual document classification process into two classes of sentiment, namely positive and negative. The data that has been obtained is then carried out a preprocessing process consisting of casefolding, tokenizing, stopword and steming and then weighted by Term Frequency-Inverse Document Frequency (TF-IDF). This study uses the Naive Bayes Classifier (NBC) classification method, which uses conditional probabilities of each word weight, and prior probabilities to determine sentiment results. From the research that has been done, the research has succeeded in making a sentiment analysis system for public figures A and B, with the results of the average accuracy of public figure A test data being 81.52% and public figure B test data 83.82% from a total of 616 test data and 154 test data. Keywords: Sentiment Analysis, classification, Public Figures, Naive bayes
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 08 Apr 2022 02:21 |
| Last Modified: | 08 Apr 2022 02:21 |
| URI: | http://eprints.uty.ac.id/id/eprint/9562 |
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