Version 1
: Received: 19 August 2024 / Approved: 19 August 2024 / Online: 20 August 2024 (09:24:01 CEST)
How to cite:
Padhy, M.; Modibbo, U. M.; Rautray, R.; Tripathy, S. S.; Bebortta, S. Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques. Preprints2024, 2024081395. https://doi.org/10.20944/preprints202408.1395.v1
Padhy, M.; Modibbo, U. M.; Rautray, R.; Tripathy, S. S.; Bebortta, S. Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques. Preprints 2024, 2024081395. https://doi.org/10.20944/preprints202408.1395.v1
Padhy, M.; Modibbo, U. M.; Rautray, R.; Tripathy, S. S.; Bebortta, S. Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques. Preprints2024, 2024081395. https://doi.org/10.20944/preprints202408.1395.v1
APA Style
Padhy, M., Modibbo, U. M., Rautray, R., Tripathy, S. S., & Bebortta, S. (2024). Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques. Preprints. https://doi.org/10.20944/preprints202408.1395.v1
Chicago/Turabian Style
Padhy, M., Subhranshu Sekhar Tripathy and Sujit Bebortta. 2024 "Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques" Preprints. https://doi.org/10.20944/preprints202408.1395.v1
Abstract
The advancements in social networking have empowered open expression on micro blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faced challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for Twitter sentiment data using word count vectorization and machine learning techniques to reduce the difficulties addressed on annotated sentiment-labelled tweets. Various classifiers (Naive Bayes, Decision Tree, K Nearest Neighbours, Logistic Regression, and Random Forest) were evaluated based on Accuracy, Precision, Recall, F1-score, and Specificity. Random Forest outperformed others with an Area under Curve (AUC) value of 0.96, and an Average Precision (AP) score of 0.96 in sentiment classification, especially effective with minimal Twitter-specific features.
Keywords
Sentiment Classification; Twitter Sentiment Analysis; Word Count Vectorization; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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