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Machine Learning-Based Sentiment Analysis of Twitter Using Logistic Regression
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Author(s): D. Kavitha (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India), Shyam Venkatraman (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India), Karthik CR (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India)and Navtej S. Nair (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India)
Copyright: 2024
Pages: 12
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch020
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Twitter sentiment analysis is crucial for understanding public opinion in the digital age. This project employs logistic regression, a machine learning approach, to identify emotions in tweets from the Sentiment 140 dataset. Exploratory data analysis (EDA) identifies patterns in emotion distribution. Various machine learning algorithms, such as logistic regression, etc., are then used to classify tweets as good, negative, or neutral. Text preprocessing techniques prepare data, but TF-IDF weights words based on their significance. The challenges include capturing the complexities of human emotions while also keeping up with the ever-changing nature of Twitter data. Despite these limitations, data analysis and logistic regression provide important insights into public sentiment, assisting decision-making in a range of businesses. Looking ahead, the study emphasises the need for additional research to strengthen sentiment analysis methodologies. This includes addressing context-dependent emotions, adapting to diverse domains, and considering ethical issues such as partiality.
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