An adaptable scheme to enhance the sentiment classification of Telugu language

Abstract

Nowadays, the big data is ruling the entire digital world with its applications and facilities. Thus, to run the online services in better way, some of the machine learning models are utilized, also the machine learning strategy is became a trending field in big data; hence the success of online services or business is based upon the customer reviews. Almost the review contains neutral, positive, and negative sentiment value; Manual classification of sentiment value is a difficult task so that the natural language processing (NLP) scheme is used which is processed using machine learning strategy. Moreover, the part of speech specification for different languages is difficult. To overcome this issue, the current research aims to develop a novel less error pruning-shortest description length (LEP-SDL) for error pruning and ant lion boosting model (ALBM) for opinion specification purpose. Here, the Telugu news review dataset adopted to process the sentiment analysis in NLP. Furthermore, the fitness function of ant lion model in boosting approach improves the accuracy and precision of opinion specification also makes the classification process easier. Thus, to evaluate the competence of the projected model, it is evaluated with recent existing works in terms of accuracy, precision, etc., and achieved better results by obtaining high accuracy and precision of opinion specification.