Sentiment-Based Natural Language Processing for Automotive Industry

Sentiment-Based Natural Language Processing for Automotive Industry

연구 분야: Artificial Intelligence

학회: International Conference on Data Science, Machine Learning and Applications

The automobile industry is an unexpectedly changing sector that has embraced the modern era to take advantage of competitive benefits. Natural language processing (NLP) performs a crucial position in information patron sentiment, as it can hit upon diffused nuances in textual content that people can also leave out or misinterpret. This paper explores how NLP can be used to tune and analyze client sentiment inside the automotive industry. We describe the exclusive types of sentiment analysis processes currently being applied in this enterprise, along with sentiment dictionaries, device learning-primarily based methods, and deep getting-to-know-primarily based strategies. We then offer numerous examples of how sentiment evaluation may be applied in the car industry, such as enhancing customer service, detecting and responding to patron reviews, and growing marketing campaigns. Finally, we speak of some of the challenges associated with sentiment evaluation inside the automotive enterprise and provide pointers for overcoming those demanding situations. Sentiment-primarily based Natural Language Processing (NLP) is a modern utility of NLP techniques for extracting sentiment from text-based resources. The utility of NLP for sentiment evaluation can provide automotive enterprise stakeholders with critical insights into customer sentiment concerning their emblem, competition, product lines, and different marketplace factors. Using sentiment-based NLP, automotive industry stakeholders can benefit from essential insights into patron sentiment and leverage them to serve their customers better.