Text analytics & natural language processing (NLP)

Text analytics and natural language processing (NLP): sentiment analysis, topic modeling, and named entity recognition
This article discusses text analytics and natural language processing techniques, such as sentiment analysis, topic modeling, and named entity recognition, and how they can be applied to gain insights from unstructured text data. The article explores various applications of these techniques in real-world scenarios, including customer feedback analysis, market research, competitive intelligence, fraud detection, and content analysis. Several case studies are presented to demonstrate how companies like Airbnb, Coca-Cola, Ford, and JP Morgan Chase have used text analytics and NLP techniques to drive business outcomes. Overall, the article emphasizes the importance of these techniques in analyzing the growing volume and complexity of text data to make data-driven decisions and stay competitive in the era of big data.