Octava, Muhammad Qois Huzyan and Prasetyo Putri, Divi Galih and Hilmy, Farhan Mufti and Farooq, Umar and Nurhaliza, Rachma Aurya and Ganjar, Alfian (2023) Web-based Sentiment Analysis System Using SVM and TF-IDF with Statistical Feature. In: 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 20-21 November 2023, Sakheer, Bahrain.
Full text not available from this repository. (Request a copy)Abstract
Social media's tendency for instant reactions can be harnessed by companies and organizations to gather feedback. Nevertheless, effectively analyzing vast amounts of social media data poses a challenge. This issue can be addressed through the use of sentiment analysis technology. In this study, a sentiment analysis model is developed, employing Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms. The study aims to investigate the impact of feature engineering on TF-IDF, by incorporating statistical features into the SVM model's sentiment analysis performance. The experimental results reveal that the prediction model utilizing the conventional TFIDF approach achieves an SVM model with an F-measure score of 84.55. Through the implementation of feature engineering, by adding max, min, and sum features, the model's performance shows a noticeable improvement, with an increase of 0.65 in the F-measure score difference. Consequently, the proposed feature engineering method positively enhances the capability of the SVM-based sentiment analysis model. To facilitate the acquisition of sentiment analysis results through user interfaces, the trained SVM model is integrated into a web-based sentiment analysis application. By doing so, the findings of this study contribute to streamlining the process of obtaining sentiment analysis results from social media data. © 2023 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 0 |
Uncontrolled Keywords: | Classification (of information); Inverse problems; Social networking (online); Support vector machines; User interfaces; Websites; Feature engineerings; Machine-learning; Sentiment analysis; Social media datum; Statistical features; Support vector machine models; Support vectors machine; Term frequencyinverse document frequency (TF-IDF); Text classification; Web based; Sentiment analysis |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Vocational School |
Depositing User: | Sri JUNANDI |
Date Deposited: | 04 Nov 2024 08:29 |
Last Modified: | 04 Nov 2024 08:29 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10487 |