Sentiment Analysis on Article Blog Post as an Application of NLP Chatbot

Abstract

Within artificial intelligence, natural language processing is a relatively recent topic. With an estimated eight billion digital voice assistants in use due to their popularity, some of the most well-known instances of natural language processing in action include Apple Siri, Amazon Alexa, and, more recently, Google Duplex. With so much information gathered from these exchanges, further research and development on Natural Language Processing may be done, and it can be used in a number of sectors, such as business, technology, and healthcare. Sentiment analysis may be used, for example, in the healthcare industry to diagnose patients and build diagnostic models for early diagnosis of chronic disease. Sentiment analysis handles these large datasets more rapidly and efficiently with the use of Natural Language Industries. Chatbots, which have uses in customer service, healthcare, education, and workplace assistance, are becoming more important entry points to digital services and information. On the other hand, not much is known about how chatbots affect people individually, in groups, or in society as a whole. Moreover, a number of problems need to be resolved before chatbots can reach their full potential. As a result, in recent years, chatbots have become a prominent area of research. We propose a research agenda that outlines future objectives and challenges for chatbot research in order to further knowledge in this rapidly expanding field of study. This proposal synthesizes years of chatbot research debate at the CONVERSATIONS workshop series. utilizing a collaborative approach for study analysis among. Sentiment analysis is a technique or procedure used to identify and extract certain subjects from spoken and written language, such as beliefs and attitudes. Sentiment analysis, broadly speaking, is the capacity to evaluate the sentiment of a subject and categorize the general polarity of the topic phrase as positive, negative, or neutral (Kang & Park, 2014). Over the past ten years, sentiment analysis has gained significant scholarly attention.