Building a Machine Learning Pipeline For NLP
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Discover the power of sentiment analysis in NLP. Learn how to determine positive, negative, and neutral sentiments in movie reviews using rule-based methods and machine learning. Utilize pandas, sklearn, and NLTK to analyze and process data, transform text, and improve performance. Streamline the process with machine learning pipelines and hyperparameter selections. Enhance your understanding with toy examples provided.
At 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.
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. We start off with a rule-based method, then use Machine Learning, explaining the connection between the two. You will use Pandas to load, analyze and process your data. Then use sklearn to transform your data with Bag-Of-Words, or Term Frequency–Inverse Document Frequency transforms, then find the Sentiment using Machine Learning. Streamline the process apply Machine learning pipelines, and perform Hyperparameter selections in one shot. Finally, use libraries like the Natural language tool kit to improve performance. Each section will have toy examples so you can better wrap your head around it.
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