Fundamentals of NLP: Rule-based Models for Sentiment Analysis
Natural Language Processing
| Intermediate
- 7 videos | 45m 37s
- Includes Assessment
- Earns a Badge
Sentiment Analysis is a common use-case within the discipline of Natural Language Processing (NLP). Here, a model attempts to understand the contents of a text document well enough to capture the feelings, or sentiments, conveyed by the text. Sentiment Analysis is widely used by political forecasters, marketing professionals, and hedge fund managers looking to spot trends in voter, user, or market behavior. You will start this course by loading and preprocessing your data. You will read in data on movie reviews from IMDB and explore the dataset. You will then visualize the data using histograms and box plots to understand review length distribution. After that, you will perform basic data cleaning on text, utilizing regular expressions to remove elements like URLs and digits. Finally, you will conduct sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseProvide an overview of sentiment analysisAnalyze and explore review dataClean data for sentiment analysis
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Perform sentiment analysis with vaderPerform sentiment analysis with textblobSummarize the key concepts covered in this course
IN THIS COURSE
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2m 4sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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7m 27sAfter completing this video, you will be able to provide an overview of sentiment analysis. FREE ACCESS
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7m 9sIn this video, you will find out how to analyze and explore review data. FREE ACCESS
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7m 28sLearn how to clean data for sentiment analysis. FREE ACCESS
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10m 21sDuring this video, you will discover how to perform sentiment analysis with VADER. FREE ACCESS
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8m 40sIn this video, you will learn how to perform sentiment analysis with TextBlob. FREE ACCESS
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2m 29sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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