Intellectual Property India Publishes Patent Application for 'An Artificial Intelligence Based System For Comment Sentiment Analysis' Filed by College Of Engineering And Management

MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202631062953 A) filed by College Of Engineering And Management, Kolaghat, West Bengal, on May 18, for 'an artificial intelligence based system for comment sentiment analysis.'

Inventor(s) include Prof. Dilip Kumar Gayen; Dr. Arko Banerjee; Prof. Kalyanasish Shee; Sreejit Gayen; and Arpita Roy.

The application for the patent was published on May 29, under issue no. 22/2026.

According to the abstract released by the Intellectual Property India: "An Artificial Intelligence Based System for Comment Sentiment Analysis The sentiment analysis system (100) for automated opinion classification comprises a User Interface (102) configured to receive a video link from a user, a Video Locating module (104) for identifying and accessing the corresponding video using the supplied link, and a Data Processing module (106) arranged to preprocess textual data through cleaning, normalization, and feature extraction. The processed data are forwarded to a Comment Retrieval unit (108), which retrieves the top 100 comments associated with the video for sentiment evaluation. These comments are then supplied to a Machine Learning Algorithm module (110), which applies trained sentiment analysis models using Support Vector Machine-based supervised learning techniques to classify the comments into predefined sentiment categories. The final Sentiment Analysis output (112) presents the results as positive, negative, or neutral sentiment to the user. The User Interface (102) further facilitates visualization and interpretation of the sentiment outcomes, ensuring the analysis is accessible and actionable. By integrating automated video identification, comment retrieval via YouTube's API, natural language processing-based data cleaning, and machine learning techniques, the system establishes a structured sentiment analysis pipeline that transforms raw user comments into meaningful sentiment insights, enabling efficient, real-time opinion analysis for scalable applications in social media monitoring, content evaluation, and decision-support systems."

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