English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm

Abstract

Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.