An Approach Based on Machine Learning for Conducting Sentiment Analysis on Twitter Data
Keywords:
Logistic Regression, Decision Tree, Fuzzy Classification, Machine Learning
Abstract
Using Twitter's primary goals as a guide, we built a real-time sentiment analysis system that labels tweets according to the emotions they convey. One more way Twitter facilitates social networking is through microblogging, which allows users to record brief status updates. The analysis of the emotions conveyed at intervals between tweets allows us to get a reflection of public attitude, which is made possible by this massive amount of usage. The goal is to find the most accurate way to examine the information by primarily applying approaches based on machine learning. Data validation, cleaning, and preparation for visual representation will be performed on the entire provided dataset after the controlled AI technique (SMLT) has been used to capture various pieces of information, such as variable ID, amount and factual strategy, missing worth medicines, and univariate examination. Through the discovery of the optimal exactness computation, our inquiry provides a comprehensive guide to sensitivity analysis of model parameters in relation to performance in sentiment analysis prediction. All of the algorithms' performance metrics, including exactness recall, f1score, sensitivity, and specificity, are also computed and compared.
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Published
2023-12-29
How to Cite
S. Suman Rajest, R. Regin, Shynu T, & Steffi. R. (2023). An Approach Based on Machine Learning for Conducting Sentiment Analysis on Twitter Data. International Journal of Human Computing Studies, 5(12), 57-76. https://doi.org/10.31149/ijhcs.v5i12.5122
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