Sentiment Analysis of Twitter Data using Support Vector Machine

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.10 · Issue 8

Published

March 2022

Access

Open Access

Licence

CC BY-NC-SA 4.0

Dr. Hemant N. Patel Dr. Amit N. Patel

The large amount of data generated by users on social networks has increasingly aroused interest in analyzing the opinions and sentiments that are being expressed. For this, one of the most used techniques is machine learning, which needs large datasets to function properly. However, few datasets for this purpose are available, limiting the development of applications in the language. Thus, this work aims to collect Twitter messages and classify their sentiments to create a dataset for the analysis of sentiments. This research work uses 2,787 messages that are publicly available at GitHub. Using the collected data, the support vector machine (SVM) classifier achieves an accuracy of 94.37%.

GitHub Machine Learning SVM Twitter

Dr. Hemant N. Patel, Dr. Amit N. Patel (2022). Sentiment Analysis of Twitter Data using Support Vector Machine. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.10, Issue 8. ISSN: 2319-4863.

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