How do people feel about a product? Use AI to get the answer
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Use AI to analyze product sentiment. Learn how to apply natural language processing (NLP) and machine learning models to determine public opinion and sentiment about products.
At a Glance
Natural language processing (NLP) is an exciting machine learning application; IBM’s Watson NLP library can greatly ease the development and deployment of NLP as part of your application. Amazon’s 5-star rating system lacks feedback for both users and buyers. What about using NLP for helpful information? In this project, we will develop an application to classify Amazon users’ emotions using Watson NLP. After conducting this project, developers will know how to build a web scraping, emotion classification embedded application.
Introduction
Natural language processing (NLP) plays a crucial role in today’s AI landscape. One such library designed for NLP tasks is Watson NLP, which offers a wide range of functions including document understanding, translation, and trust assessment. This library simplifies the development and deployment process of NLP models. You can use the Watson NLP library to enhance your application with sophisticated NL functionality to deliver better value to the users of your application.
A Look at the Project Ahead
- Web scrapping using BeautifulSoup
- Emotion classification using Watson_nlp
- Build a simple but real functional application
What You’ll Need
Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.
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