Mining Emotions ‘A Comprehensive Study on Sentiment Analysis of Social Media’
Vyas More1, Omkar Nalawade2, 3Dr. Rupali Kalekar3
1Vyas More (MCA) ZIBACAR
2Omkar Nalawade (MCA) ZIBACAR
3Dr. Rupali Kalekar (MCA) ZIBACAR
Abstract –
Sentiment Analysis (SA), or opinion mining, is a key area of Natural Language Processing (NLP) that focuses on identifying and interpreting emotions, attitudes, and subjective information from text. With the exponential rise of social media platforms such as Twitter/X, Facebook, Instagram, Reddit, and TikTok, billions of users generate continuous streams of short, unstructured and highly contextual posts. These platforms have transformed sentiment analysis into a crucial tool for understanding public opinion, behavioral patterns, and emotional trends on a large scale. This study presents a comprehensive study of how SA techniques are applied to social media data to “mine emotions” effectively.
This study explores the evolution of sentiment analysis, beginning with traditional lexicon-based and classical machine learning methods and progressing to state-of-the-art deep learning architectures, such as RNNs, LSTMs, CNNs, and Transformer-based models (BERT, ROBERTA, GPT). It also examines multimodal approaches that combine text with images, videos, and audio to improve accuracy in real-world social-media environments. A detailed survey of widely used datasets, including Twitter Sentiment140, Semeval, IMDb, SST, and multimodal datasets, is provided alongside commonly used tools, libraries, and evaluation metrics.
This study further investigates domain-specific applications where sentiment analysis plays a critical role, including digital marketing, political opinion tracking, product review mining, crisis detection, public health monitoring, and customer experience management. Key challenges, such as noisy text, emojis and slang, code-mixed language, sarcasm, fake accounts, data imbalance, and evolving social media trends, are analyzed in depth. This study also highlights the ethical considerations related to privacy, bias, and responsible AI use.
Overall, this study aims to serve as both an accessible introduction for beginners and a detailed reference for researchers. By reviewing foundational principles, advanced computational techniques, practical applications, and open challenges, this paper provides a holistic understanding of sentiment analysis for social media emotion mining in the modern digital era.
Keywords: Sentiment Analysis, Social Media Mining, Emotion Detection, NLP, Machine Learning, Deep Learning, Multimodal Analysis
