Offered By: IBM
Building a Machine Learning Pipeline For NLP
Natural language processing (NLP) is a part of artificial intelligence concerned with understanding written text. Sentiment analysis is an important part of NLP that identifies the emotional tone behind a body of text and is used in customer reviews and survey responses, online and social media. In this project, you will determine the sentiment of movie reviews as positive, negative, and neutral with the rule-based method, then use Machine Learning. You will use pandas to load and analyze data and sklearn to process and classify the text and work with other libraries like NLTK.
Continue readingGuided Project
Artificial Intelligence
250 EnrolledAt a Glance
Natural language processing (NLP) is a part of artificial intelligence concerned with understanding written text. Sentiment analysis is an important part of NLP that identifies the emotional tone behind a body of text and is used in customer reviews and survey responses, online and social media. In this project, you will determine the sentiment of movie reviews as positive, negative, and neutral with the rule-based method, then use Machine Learning. You will use pandas to load and analyze data and sklearn to process and classify the text and work with other libraries like NLTK.
Why you should do this Guided Project
A Look at the Project Ahead
- Understand Sentiment analysis
- Apply pandas to load,analyze and process your dataÂ
- Understand text preprocessingÂ
- Understand the connection between rule-based methods and Machine Learning based methodsÂ
- Understand and Apply Bag-Of-Words and Term Frequency–Inverse Document Frequency to Sentiment analysis using
- Apply Hyperparameter using scikit-learn to NLPÂ
- Apply Machine Learning pipeline using scikit-learn to NLPÂ