Natural Language Processing for Sentiment Analysis in Social Media

Abstract

The monograph “Natural Language Processing for Sentiment Analysis in Social Media” makes a key
contribution to social media research by highlighting that relying solely on sentiment analysis for customer
insights might be less effective than human analysis of consumer conversations. This monograph emphasizes the potential superiority of Natural Language Processing (NLP) over traditional sentiment analysis, offering valuable insights for enhancing social media monitoring practices. By incorporating various aspects like different parts of speech, dimensionality reduction, proper model training, and noise-free data, the proposed model achieves improved sentiment analysis performance. Although acknowledging limitations, such as the need for varied datasets, the study underscores NLP's potential for enhancing sentiment analysis on social media.