KNetwork: advancing cross-lingual sentiment analysis for enhanced decision-making in linguistically diverse environments

Sentiment analysis is pivotal in facilitating informed decision-making for businesses, governments, and organizations by comprehending public opinion. However, the task becomes challenging when dealing with linguistic diversity and limited resources for specific languages. This paper presents a novel method, KNetwork, for conducting cross-lingual sentiment analysis of Hindi and English text. The KNetwork leverages the feature vectors generated from translated and transliterated text, aiming to enhance the accuracy of sentiment analysis in cross-lingual settings. Specifically, this paper addresses the challenges associated with sentiment analysis in countries like India, which possess a rich linguistic heritage. The KNetwork model is rigorously evaluated on multiple review datasets, showcasing its performance against state-of-the-art models. Moreover, KNetwork achieves superior results in terms of accuracy of 92.5% and an F1-score of 0.922, outperforming existing models. With an AUC-ROC value of 0.934, it excels in cross-lingual sentiment analysis. This study advances the sentiment analysis for languages with limited resources and underscores the KNetwork’s efficacy in enhancing accuracy, with far-reaching implications for informed decision-making.