Sentiment Analysis of Social Media Data Using Machine Learning Techniques
Sentiment Analysis of Social Media Data Using Machine Learning Techniques
Authors:
H. Ashish Kumar1, Loka Charan Reddy2
1Assistant Professor, Department of Computer Science and Engineering, St. Martin’s Engineering College
2UG Student, Department of Computer Science and Engineering, St. Martin’s Engineering College
Email: ashishkumarhoskerycse@smec.ac.in1, lokacharan8@gmail.com2
Abstract:
Sentiment analysis of social media data has emerged as a crucial task for understanding public opinion, customer preferences, and emerging trends in the digital era. With the rapid growth of platforms such as Twitter, Facebook, and Instagram, vast amounts of user-generated content are produced daily, making manual analysis impractical. This study focuses on applying machine learning techniques to automatically classify and interpret sentiments expressed in social media posts.
The proposed approach involves collecting large-scale textual data from social media platforms, followed by preprocessing steps such as tokenization, stop-word removal, and stemming to enhance data quality. Various supervised machine learning algorithms, including Naïve Bayes, Support Vector Machines (SVM), and Logistic Regression, are employed to categorize the data into positive, negative, or neutral sentiments. Feature extraction techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings are utilized to improve model performance.
The effectiveness of these models is evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that machine learning-based approaches can achieve high accuracy in sentiment classification, with certain models outperforming others depending on the dataset characteristics. Additionally, the study highlights challenges such as handling sarcasm, slang, and multilingual content in social media data.
Overall, this research emphasizes the potential of machine learning techniques in automating sentiment analysis and providing valuable insights for businesses, policymakers, and researchers. Future work may explore the integration of deep learning models and real-time analysis systems to further enhance performance and scalability.