Sentiment Analysis on Twitter Using Machine Learning Techniques and TF-IDF Feature Extraction: A Comparative Study
ahmed, W., Semary, N., Amin, K., Adel Hammad, M. (2023). Sentiment Analysis on Twitter Using Machine Learning Techniques and TF-IDF Feature Extraction: A Comparative Study. EKB Journal Management System, 10(3), 52-57. doi: 10.21608/ijci.2023.236052.1128
wesam ahmed; Noura A. Semary; Khalid Amin; Mohamed Adel Hammad. "Sentiment Analysis on Twitter Using Machine Learning Techniques and TF-IDF Feature Extraction: A Comparative Study". EKB Journal Management System, 10, 3, 2023, 52-57. doi: 10.21608/ijci.2023.236052.1128
ahmed, W., Semary, N., Amin, K., Adel Hammad, M. (2023). 'Sentiment Analysis on Twitter Using Machine Learning Techniques and TF-IDF Feature Extraction: A Comparative Study', EKB Journal Management System, 10(3), pp. 52-57. doi: 10.21608/ijci.2023.236052.1128
ahmed, W., Semary, N., Amin, K., Adel Hammad, M. Sentiment Analysis on Twitter Using Machine Learning Techniques and TF-IDF Feature Extraction: A Comparative Study. EKB Journal Management System, 2023; 10(3): 52-57. doi: 10.21608/ijci.2023.236052.1128

Sentiment Analysis on Twitter Using Machine Learning Techniques and TF-IDF Feature Extraction: A Comparative Study

IJCI. International Journal of Computers and Information
Article 8, Volume 10, Issue 3, November 2023, Page 52-57  XML PDF (554.94 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.236052.1128
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Authors
wesam ahmed email 1; Noura A. Semary2; Khalid Aminorcid 3; Mohamed Adel Hammadorcid 4
1Wesam Ahmed ,Information Technology University of South valley,Mansoura, Egypt
2Inf. tech. dept. , Information and computersInformation Technology Menoufia University Menoufia ,Egypt faculty, Menofia university
3Information Technology dept., Faculty of Computers and Information, Menoufia University, Egypt
4Faculty of computers and information, Menofia university
Abstract
The term "machine learning" refers to a sort of artificial intelligence (AI) that empowers software applications to enhance their predictive capabilities without explicit programming for such purposes. In order for machine learning algorithms to anticipate future output values, they require past data as input. In terms of scope, this research falls under sentiment analysis. The latter field is becoming increasingly active in terms of extracting people's opinions on issues related to politics, economics, and social issues. The purpose of sentiment classification is to categorize users' opinions as neutral, positive, or negative based on textual input alone. Despite these advantages, the accuracy and effectiveness of sentiment analysis are compromised by the obstacles encountered in the field of natural language processing (NLP). Recent research has shown that machine learning algorithms can assist in NLP. In this research, we investigate a range of machine-learning strategies for solving sentiment analysis challenges. Two datasets were analyzed with the models based on the term frequency-inverse document frequency (TF-IDF). A comparison study was conducted between each of the models to determine how they performed in experiments. Regarding accuracy and F1 score, logistic regression performs better than other algorithms.
Keywords
TF-IDF; AI; sentiment analysis; machine learning; NLP
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