TY - GEN
T1 - Sentiment Analysis in Twitter Data Using Machine Learning-Based Approach
AU - Al Arafat, Kazi Abdullah
AU - Roni, Mahmudur Rahman
AU - Siddique, Sumaya
AU - Yousuf, Mohammad Abu
AU - Moni, Mohammad Ali
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - There are now many more people sharing their views, ideas, and opinions because of the proliferation of user-generated material on the Internet and the development of information technology. People frequently use social media platforms like Facebook, Instagram, WhatsApp, and Twitter to express their thoughts and feelings. Opinion analysis, which is the act of identifying emotional undertones from this enormous volume of Internet data, has become more important as a way of understanding public opinion, trends, and brand impressions. However, because of the distinctive features of the network, such as informal language, character constraints, and the quick influx of data, conducting sentiment analysis on Twitter data poses unique obstacles. By considering the above difficulties, we have proposed a machine learning-based approach to classify the emotions as either positive, negative, or neutral. This paper conducts a thorough analysis of sentiment on Twitter data using the Support Vector Machine (SVM), Maximum Entropy (Max Ent), and Naive Bayes Multinomial (NBM) machine learning algorithms. To do this experiment, we deal with the large volume of data collected from Kaggle. To train the machine learning model, this wide range of tweet datasets from different subjects and domains is preprocessed using the Natural Language Tool Kit (NLTK). The results from these models were tested using various testing matrices like precision, recall, and F1-score. We achieved a maximum accuracy of 97% for SVM compared to all three classifiers.
AB - There are now many more people sharing their views, ideas, and opinions because of the proliferation of user-generated material on the Internet and the development of information technology. People frequently use social media platforms like Facebook, Instagram, WhatsApp, and Twitter to express their thoughts and feelings. Opinion analysis, which is the act of identifying emotional undertones from this enormous volume of Internet data, has become more important as a way of understanding public opinion, trends, and brand impressions. However, because of the distinctive features of the network, such as informal language, character constraints, and the quick influx of data, conducting sentiment analysis on Twitter data poses unique obstacles. By considering the above difficulties, we have proposed a machine learning-based approach to classify the emotions as either positive, negative, or neutral. This paper conducts a thorough analysis of sentiment on Twitter data using the Support Vector Machine (SVM), Maximum Entropy (Max Ent), and Naive Bayes Multinomial (NBM) machine learning algorithms. To do this experiment, we deal with the large volume of data collected from Kaggle. To train the machine learning model, this wide range of tweet datasets from different subjects and domains is preprocessed using the Natural Language Tool Kit (NLTK). The results from these models were tested using various testing matrices like precision, recall, and F1-score. We achieved a maximum accuracy of 97% for SVM compared to all three classifiers.
KW - Machine learning
KW - Maximum Entropy (Max Ent)
KW - Naive Bayes Multinomial (NBM)
KW - Natural language processing (NLP)
KW - Sentiment analysis
KW - Support Vector Machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85207841389&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85207841389&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-3937-0_12
DO - 10.1007/978-981-97-3937-0_12
M3 - Conference paper
AN - SCOPUS:85207841389
SN - 9789819739363
T3 - Lecture Notes in Networks and Systems
SP - 169
EP - 184
BT - Proceedings of Trends in Electronics and Health Informatics - TEHI 2023
A2 - Mahmud, Mufti
A2 - Kaiser, M. Shamim
A2 - Bandyopadhyay, Anirban
A2 - Ray, Kanad
A2 - Al Mamun, Shamim
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023
Y2 - 20 December 2023 through 21 December 2023
ER -