Opinion Mining
Project Title: Opinion Mining: Movie Review Analysis
Project Duration: October 2015 - December 2015 (3 months)
Project Type: Academic Project
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Technology Used: Java, Weka (Tool for visualization)
Find Implementation of Algorithm Here (GitHub Code link): [code]
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Colleagues:
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Rohit Maurya
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Arshpreetsingh Pabla
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Abstract:
To analyse the movie reviews and classify the movie as positive or negative according to the reviews given to it on scale of 5.
Opinion Mining Steps:
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Collection of data
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Pre-process the data
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Train the model
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Testing the unknown data
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Analysis of the movie
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Basic Opinion Rules to be followed:
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Neg -> Negative
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Pos -> Positive
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Negation Neg -> Positive
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Negation Pos -> Negative
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Desired value range -> Positive
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Below or above the desired value range -> Negative
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Decreased Neg -> Positive
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Decreased Pos -> Negative
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Increased Neg -> Negative
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Increased Pos -> Positive
Removal of Stop words:
Stop words usually refer to the most common words in a language, there is no single universal list of stop words used by all processing of natural language tools, and indeed, not all tools even use such a list. Some tools specifically avoid removing these stop words to support phrase search.
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Classifier: We have used J48 as our classifier to train the data. It uses C4.5 algorithm for building decision trees.
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Filter: We have used StringToWordVector().
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Applications:
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Social Media Analysis
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Good Database
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Detecting and understanding how the audience is reaching out to a brand, whether positively or negatively.
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TweetFeel – Twitter Search with Feelings
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TweetFeel gathers real-time Twitter data about whatever search term the user has entered, and then evaluates those tweets for positive and negative feelings.
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Learn faster from Customer Feedback
