Sentiment analysis of microblog text based on sentiment dictionary Gnanapriya S, Anandan K E Learning and Digital Media, 2025 This study introduces a method for sentiment analysis applied to microblog texts, leveraging a sentiment dictionary. Focused on discerning sentiments as positive, negative, or neutral, the research incorporates a sentiment dictionary tailored for microblog content. The experiment employs a dataset pertaining to the new coronavirus epidemic collected from a microblogging platform for testing and compares the outcomes with those of existing methods. Key enhancements include the integration of a microblog-specific emotion dictionary and the formulation of semantic rules designed for the nuances of Chinese text. Results demonstrate improved accuracy in text emotion recognition, outperforming traditional methods and achieving notable accuracies for positive, negative, and neutral sentiments. Objective: To develop and implement a robust sentiment analysis model for microblog text, leveraging a comprehensive sentiment dictionary to accurately classify the emotional tone and polarity of textual data. The model aims to enhance the understanding of user sentiments on microblogging platforms by examining the nuances of text and identifying positive, negative, or neutral sentiments. The analysis will facilitate the extraction of insights from large volumes of user-generated content, enabling applications such as social media monitoring, brand perception analysis, and opinion tracking.
Sentiment analysis in political discourse: Understanding public opinion from social media S Gnanapriya, K Anandan E Learning and Digital Media, 2025 The exponential growth of social media platforms has fundamentally transformed the landscape of political discourse and public opinion formation, generating unprecedented volumes of data that reflect citizen sentiments toward political issues, candidates, and policies. This comprehensive research investigates the application and effectiveness of sentiment analysis techniques in extracting and analyzing public opinion from social media platforms within political discourse. Through a mixed-methods approach combining advanced natural language processing techniques with traditional statistical analysis, this study examines over 10 million social media posts across multiple platforms during the 2023-2024 election cycle. The research employs sophisticated machine learning algorithms and deep learning models, including BERT-based sentiment classifiers and attention mechanisms, to capture nuanced public opinions and emotional responses to political events, policy announcements, and campaign messages. Our findings reveal significant correlations between social media sentiment patterns and electoral outcomes, with a predictive accuracy of 78.3% for major political events. The study also uncovers important demographic variations in sentiment expression across different social media platforms and identifies key challenges in sentiment analysis, including the impact of echo chambers and algorithmic bias. This research contributes to the growing field of computational political science by demonstrating the potential of automated sentiment analysis in understanding public opinion while highlighting the importance of considering contextual factors and platform-specific characteristics in sentiment analysis implementations. Furthermore, our research introduces novel methodological approaches for handling multilingual political discourse and cross-platform sentiment analysis, addressing critical gaps in existing literature and providing practical frameworks for future research in this domain.
SWARM OPTIMIZED MACHINE LEARNING MODEL FOR ENHANCED PREDICTION OF CORONARY ARTERY DISEASE Journal of Theoretical and Applied Information Technology, 2025
Robustious feature selection based genetic algorithm (RFS-GA) for cross domain opinion mining , Dr. E. Chandra blessie, S. Gnanapriya, and International Journal of Recent Technology and Engineering, 2019 Day by day the requirement of information for processing the sentiment analysis is getting increased multiple times. For these kind of reasons, feature selection is utilized to detect the opinion among different reviews and comments. Sentiment analysis is becoming like phenomenon due to increase of social media’s popularity. Currently, significant advancements are shown in this research domain, but still multiple challenges are to be solved – i.e., sentiment analysis in cross domains. In this paper rumbustious feature selection based genetic algorithm is proposed to address the problem of analyzing the sentiments in cross domain. It performs classification based optimistic-class and pessimistic-class. The dataset used to this research work includes books, DVDs, gadgets and kitchen appliances. Initially the features selection is performed and opinion mining is performed by Genetic Algorithm. Benchmark performance metrics are selected for measuring the performance of proposed work against existing method. Results depict that the proposed work has better performance than that of the existing work as far as chosen performance metrics.
Genetic algorithm based ant colony optimization (GA-ACO) for cross domain opinion mining Arpn Journal of Engineering and Applied Sciences, 2019
Feature selection using modified ant colony optimization approach (FS-MACO) based five layered artificial neural network for cross domain opinion mining Journal of Theoretical and Applied Information Technology, 2018
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