About This Class
Build an automated system for analyzing text documents and finding the polarity of the documents. This course delves into the evolving area of sentiment analysis. The course starts with the basics of sentiment analysis and natural language processing and covers both lexicon based approach and machine learning based methods of sentiment analysis. By the end of this course you will be conversant with popular python libraries such as NLTK, VADER, TextBlob and Sklearn and should be able to build a sophisticated sentiment analyzer with reasonable accuracy.
You can expect to gain the following skills from this course
Data mining using web-scraping
Natural language processing basics
Lexicon based sentiment analysis
Machine learning based sentiment analysis
Using VADER and TextBlob libraries to perform sentiment analysis
Naive Bayes algorithm
- Building machine learning based sentiment analyzer
Course image by courtesy of rawpixel.com
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All python codes and links discussed in the lecture videos could be found in the zip file titled "python_scripts_and_links.zip".
Using your knowledge of web scraping (use attached python script as reference) extract the reviews of the movie Joker from the link https://www.imdb.com/title/tt7286456/reviews?ref_=tt_urv.
Using Vader Library, analyze the sentiment of the first 100 reviews. Compare the performance of your sentiment analysis with the actual review.