Sentiment and Emotion Analysis on Consumer Review using NRCLex

Abstract

As a result of the Internet's explosive expansion, social networking sites have emerged as a crucial tool for expressing emotions to individuals around the globe. Many people share their reviews or points of view through text, photographs, music, and video. Consumer review sentiment and emotion analysis is a popular approach for determining customer preferences and feedback. We used NRClex in this research to assess customer reviews and categories them into several sentiment categories, including positive, negative, and neutral sentiment, as well as other emotional categories, like joy, anger, and sadness. The pre-processing of the text data entails eliminating stop words, punctuation, and other noise. The remaining words are then mapped using the NRClex lexicon to their appropriate emotional and sentiment ratings. The analysis' findings may be used to learn more about consumer attitudes and preferences, spot potential problems with a good or service, and develop marketing strategies. Nonetheless, sentiment and emotion analysis utilizing NRClex may help guide decision-making across a variety of sectors by offering useful insights into the emotions and sentiment represented in text data. NRCLex also provides methods for changing the emotion categories and score system in order to meet certain use cases. NRCLex is a powerful and flexible toolkit for text data semantic and emotional analysis. It is especially beneficial for applications that need speedy and accurate analysis of huge amounts of text, such as market research, customer feedback analysis, and social media monitoring.