MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541124246 A) filed by Dr. S Anitha; Dr. S. Santhosh Baboo; and Dr. Mary Metilda, Chennai, Tamil Nadu, on Dec. 9, 2025, for 'evaluation of sentiment in tweets using nature-inspired metaheuristic algorithms and feature selection.'
Inventor(s) include Dr. S Anitha; Dr. S. Santhosh Baboo; and Dr. Mary Metilda.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "Evaluation of Sentiment in Tweets Using Nature-Inspired Metaheuristic Algorithms and Feature Selection Abstract: Twitter is a widely used social network that generates a vast number of posts. Sentiment analysis of these tweets is highly valuable across social media. The insights gained are applied in engineering, management, social, and economic fields by examining tweets according to user needs. However, classifying Twitter data is a complex task. Data optimization plays a key role in many research areas. Conducting sentiment analysis on Twitter datasets is often time-consuming. To address this, feature selection is employed to enhance both the speed and accuracy of classification. Common feature selection methods, such as Information Gain (IG), Document Frequency thresholding, and Chi Square, assign scores to features using specific statistical formulas. Relevant features are then chosen from a ranked set based on a user-defined threshold, which can influence classification performance. Moreover, selecting an optimal feature subset from a high-dimensional space is a challenging optimization problem. It aims to eliminate irrelevant and noisy features, thereby improving classification accuracy and reducing processing time. Nature-inspired optimization algorithms are recognized as effective tools for this purpose. Data obtained from Twitter can be used to analyze user opinions. Social media content is examined to classify user perspectives as either positive or negative. Twitter users frequently share views on current topics such as government policies, political issues, and new product reviews. This information serves as a valuable resource to inform decision-making processes. Evolutionary Intelligence (EI) is well-suited for analyzing such datasets, which often vary in size and content. In this paper, Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Firefly Algorithm (FA) are applied to identify the most relevant features. The structure of this work is as follows: reviews existing research on sentiment analysis that integrates feature selection techniques-such as PSO, FA, and CS-with classification methods like Support Vector Machines (SVM) and Naive Bayes (NB). It provides a detailed explanation of the proposed methodology, including its mathematical formulation."
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