SentiNet: A Robust and Multilingual Sentiment Analysis System with Transfer Learning and Adversarial Training Techniques

Sentiment analysis, the task of automatically classifying the sentiment expressed in a text, plays a vital role in understanding public opinion and sentiment trends across various domains and languages. However, developing robust and multilingual sentiment analysis systems remains a challenging task due to the scarcity of labeled data and the linguistic complexities across different languages. This research paper introduces SentiNet, a novel sentiment analysis system that addresses these challenges through the integration of four-component framework for sentiment analysis. The Emotional Identifier accurately classifies emotions using advanced deep learning models. The Model Consistency Enhancer reduces biases and inconsistencies by combining multiple sentiment classifiers and subjecting them to adversarial attack testing. The SentSim Generator generates contextually similar sentences to enrich the analysis. cross-lingual sentiment transfer tool that can modify the sentiment of a text across multiple languages while preserving its meaning. Extensive evaluations on benchmark datasets demonstrate the superiority of SentiNet over state-of-the-art methods, with improved emotion detection, enhanced model consistency, and diverse sentence variations. SentiNet contributes to sentiment analysis advancements and establishes itself as a powerful tool for accurate sentiment analysis. The framework's applicability extends to various domains requiring sentiment analysis for decision-making and understanding user sentiments.