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Data Science Project-Movie Review Sentiment Analysis using R

Learn to classify the sentiment of sentences from the Rotten Tomatoes dataset. You will be 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.

Prerequisites

  • Language used: R

Project Description

With the increasing usage of Social Media such as Twitter and review websites like yelp and rotten tomatoes, it has become important to glean insights from the huge amounts of subjective opinionated data. 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. You will get 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 data science project  challenging.

Instructors

 
Shubham

Statistical Analyst SME

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