Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

Rusydi Umar; Sunardi; Muhammad Nur Ardhiansyah Nuriyah

doi:10.29207/resti.v7i4.4931

Rusydi Umar Telkom University
Sunardi Telkom University
Muhammad Nur Ardhiansyah Nuriyah Universitas Ahmad Dahlan

DOI: https://doi.org/10.29207/resti.v7i4.4931

Keywords: Sentiment Analysis, Twitter, SVM, K-NN

Abstract

On Twitter, users can post tweets, videos, and images. It can, however, also be disruptive and difficult. To categorize the material and improve searchability, hashtags are crucial. This study focuses on examining the opinions of Twitter users who participate in trending topics. The algorithms K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used for sentiment analysis. The data set comprises tweet information on popular topics that was collected using the Twitter API and saved in Excel format. SVM and K-NN are used for data preparation, weighting, and sentiment analysis. With 105 data points, the study provides insight into user sentiment. SVM identified 99% of positive responses and 1% of negative responses with an accuracy of 80%. KNN successfully identified 90% of the positive responses and 10% of the negative responses, with an accuracy rate of 71.4%. According to the results, SVM performs better when analyzing the sentiment of hashtag users on Twitter.

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