Sentiment Analysis on Social Media
Authors:-Mr.Shahbaz Ahmad, Assistant Professor Ms.Noorishta Hashmi, Assistant Professor Mr.Ehteshaam Hussain
Abstract-The rise of social media platforms has revolutionized communication, enabling individuals to share opinions, emotions, and experiences in real-time. With billions of users generating vast amounts of unstructured data daily, social media has become a rich resource for understanding public sentiment and social behavior. Sentiment analysis, a subfield of natural language processing (NLP), offers a computational approach to identifying and categorizing sentiments expressed in text data. This research focuses on the development and application of sentiment analysis techniques to analyze user-generated content on platforms such as Twitter, Facebook, and Reddit. By utilizing machine learning, lexicon-based methods, and deep learning approaches, this study aims to assess the effectiveness of various sentiment classification models. Techniques including Support Vector Machines (SVM), Naïve Bayes, Long Short- Term Memory (LSTM) networks, and BERT are evaluated using benchmark datasets. The paper also addresses the challenges inherent in sentiment analysis, such as sarcasm, slang, multilingual content, and data imbalance. The results demonstrate that context-aware models like BERT significantly outperform traditional approaches in detecting nuanced sentiments. The findings of this research have applications in fields such as brand monitoring, political analysis, customer feedback evaluation, and disaster response. Furthermore, the study emphasizes the ethical implications of mining and analyzing social media data, advocating for transparency, consent, and responsible data handling. Sentiment analysis, also known as opinion mining, has emerged as a critical tool in natural language processing (NLP) for extracting subjective information from social media platforms. The exponential growth of user-generated content on platforms such as Twitter, Facebook, and Instagram has made sentiment analysis indispensable for businesses, governments, and researchers seeking to understand public opinion, brand perception, and emerging trends. This paper provides a comprehensive review of sentiment analysis techniques, challenges, and applications in the context of social media, while also discussing future research directions to enhance accuracy and scalability.
