A COMPARATIVE SAAS FRAMEWORK FOR REAL-TIME SOCIAL MEDIA SENTIMENT ANALYSIS USING MULTI-MODEL APPROACH - Mukherjee, Papiya; Saini, Parul; Tyagi, Priyanka; Singh, Shweta; Srivastava, Varsha; Islam, Mamunur (ShodhKosh: Journal of Visual and Performing Arts, 2026)
The process of examining a piece of text to identify whether the underlying sentiment expressed is positive, negative, or neutral is known as sentiment analysis. While it may seem straightforward, sentiment analysis is currently playing a vital role in comprehending how people perceive and interact on social media platforms, which is critical information for businesses and content creators. By gauging the sentiments conveyed through textual data, companies and individuals creating online content can gain valuable insights into the emotional responses and engagement levels of their target audiences. This paper presents our platform “Analytix” which is designed for analyzing sentiments across YouTube, Facebook, Reddit, Twitter and WhatsApp etc. It supports 197 languages and can handle multiple languages at once. The core of our platform is its user-friendly dashboard that pulls out the sentiments (positive, negative, or neutral) from posts across different social media platforms. It offers users the flexibility to choose from three different model options for sentiment analysis: GPT, transformer models, and a custom proprietary sentiment analysis model developed inhouse. It deeply analyzes each post to identify the overall sentiment people have towards it