MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118905 A) filed by Mlr Institute Of Technology; and Marri Laxman Reddy Institute Of Technology And Management on October 05, 2026, for A System And Method For Climate Change Sentiment Analysis Using Domain-Specific Bidirectional Encoder Representations From Transformers (climatebert).
Inventors include Dr. P. Subhashini; Ms. J. Priyanka G; Mr. M. Jeevan; and Mr. B. Varun.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: The present invention relates to a computer-implemented system and method for climate change sentiment analysis using Domain-Specific Bidirectional Encoder Representations from Transformers (ClimateBERT). The system is configured to acquire climate-related textual data from social media platforms and process the acquired data using natural language processing techniques comprising text cleaning, tokenization, stop-word removal, stemming, and lemmatization. The processed textual data is transformed into numerical representations using Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and ClimateBERT embeddings. The generated representations are provided to one or more machine learning classifiers comprising Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression for classification of the textual data into sentiment categories comprising positive, negative, and neutral sentiments. ClimateBERT provides domain-specific contextual representations of climate-related expressions for improving the semantic analysis and sentiment classification of climate-related textual content. The system further comprises a visual analytics module configured to generate interactive representations of sentiment distributions, trends, and related analytical parameters. The proposed system provides an automated, scalable, and context-aware mechanism for processing and classifying large-scale climate-related textual data.
Disclaimer: Curated by HT Syndication.
