Sentiment Scope: Context-Aware Social Media Sentiment Analysis Using Transformer Models

Authors:

Mrs B. Prashanthi, Dasari Hansika

Assistant Professor, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India bprasanthicse@smec.ac.in

Student, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India hansikadasari78@gmail.com

Abstract:

The rapid growth of social media platforms has resulted in an enormous amount of user- generated textual data expressing opinions, emotions, and attitudes about various events, products, and social issues. Analyzing this data through sentiment analysis helps organizations and researchers understand public opinion and behavioral trends. However, traditional sentiment analysis techniques often struggle to capture contextual meaning, sarcasm, and complex linguistic patterns present in social media text. To address these challenges, this study proposes a context-aware sentiment analysis approach using transformer-based models. Transformer architectures such as Bidirectional Encoder Representations from Transformers (BERT) and related models utilize self-attention mechanisms to capture contextual relationships between words in a sentence. These models are capable of understanding long- range dependencies and semantic context more effectively than traditional machine learning approaches. In this research, social media textual data is preprocessed through cleaning, tokenization, and normalization techniques before being analyzed by transformer-based models for sentiment classification. The proposed system aims to classify social media posts into sentiment categories such as positive, negative, and neutral while considering contextual dependencies within the text. Performance evaluation is conducted using standard metrics including accuracy, precision, recall, and F1-score. The results demonstrate that transformer- based sentiment analysis models significantly improve contextual understanding and classification performance in social media data. This study highlights the potential of transformer models for developing more accurate and context-aware sentiment analysis systems for real-world applications.

Keywords: Sentiment Analysis, Transformer Models, Context-Aware Analysis, Social Media Mining, Natural Language Processing, BERT.