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Perform Sentiment Analysis on Rotten Tomatoes using K-means Clustering in Python

This competition presents a chance to benchmark your sentiment-analysis ideas on the Rotten Tomatoes dataset. You are asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive.
4.64.6

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What will you learn

  • Text Mining and
  • Sentiment Classification
  • Information extraction from Text
  • Learn use of library "tm"
  • Learn use of libraries wordcloud, cluster
  • Hierarchical Clustering
  • K-mean clustering

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

The Rotten Tomatoes movie review dataset is a corpus of movie reviews used for sentiment analysis, originally collected by Pang and Lee. In their work on sentiment treebanks, Socher et al. used Amazon's Mechanical Turk to create fine-grained labels for all parsed phrases in the corpus. This competition presents a chance to benchmark your sentiment-analysis ideas on the Rotten Tomatoes dataset. You are asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive. Obstacles like sentence negation, sarcasm, terseness, language ambiguity, and many others make this task very challenging.

Instructors

 
Shubham

Statistical Analyst SME

"He is currently associated with International Store Analytics lab. He is passionate about analytics"