Sentiment Analysis for Promoting the Manufacturing Sector

Résumé fourni par la source

In the modern world, sentiment analysis has emerged as a popular research tool for extracting, quantifying, and identifying useful subjective information. The world we currently inhabit has been transformed by the internet, and the number of internet users have increased manifold in the last decade. Users learn about the most recent events mainly through social media, with everything that occurs around us becoming instantly visible on the platform. The manufacturing sector can use these reviews and feedbacks for improving and transforming the business and reaching new heights. Nowadays, there are numerous social media platforms where we may express our thoughts and opinions on a given subject and get fresh information. Any product or service that a business offers is subject to people’s views. In this chapter, the role of sentiment analysis in the manufacturing industry has been elaborated, and various techniques used to perform sentiment analysis on customer reviews have been discussed. In order to evaluate the value of currently available lexical resources, as well as features that capture specifics about the informal and creative language used in microblogging, a hybrid approach utilizing the ensemble learning method (ELM) has been proposed. Experiments have also been conducted on the product sentiment analysis dataset. According to the experimental findings, support vector machine (SVM) and logistic regression (LR) techniques outperform other state-of-the-art techniques, and the hybrid ensemble learning model showed significant improvement over all other individual learning models.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.