1 Tweets Classification Supervisor - Dr. Vikas Saxena Name - Shubhangi Agarwal Varun Ajay Gupta
Nov 07, 2014
1
Tweets Classification
Supervisor - Dr. Vikas SaxenaName - Shubhangi Agarwal Varun Ajay GuptaEnrolment No. – 10104768 10104730
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Introduction• As we are living in an era of social networking
that’s why our project focuses on twitter. In this project we extracts the tweets and then classify them into different categories . As with extraction of tweets we extracts the huge amount of information with it.
• By using tweet classification we can predict the current trend like which is most popular language on twitter, most talked about person , burning topics and much more.
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Problem Statement
• Extraction of tweets.• Converting unstructured data into structured
data.• Pre-processing of data .• Finding the most popular language on twitter.• Choosing of features for the classification.• Classifying the tweets into different categories.
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Algorithm • SVMs (support vector machines) are supervised
learning models with associated learning algorithms that analyse data and recognize patterns, used for classification and regression analysis .
• Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other,
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Why SVM ?
• Most popular in text classification.• High accuracy in comparison to other algorithms.• By choosing right features svm can be robust
even when the training sample has some bias.
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Technology Used
• Operating System: UBUNTU 12.04 .• Language: PYTHON• Tools: GEDIT• Debugger: PYTHON DEBUGGER
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Unstructured Tweets
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Structured Tweets
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Calculating most popular language on
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Pictorially showing popularity of
languages
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Features choose• No of sports words.• No of politics words.• No of entertainment words.• Lexical complexity.• No of hash tags.• No of digits.
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Values of features of training set
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Feature values of testing data set before
application of SVM
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Result of classification of tweets
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Graph of SVM and accuracy
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ConclusionOn implementing the SVM on the testing dataset . It classifies the data into sports ,entertainment and politics category with a accuracy of 97.5%
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Future Work • Till now we have implemented the SVM to classify
the tweets in general categories like Sports , politics , entertainment. We will try to implement it to categories data into more specific categories so that it can be used by the marketing and PR team of different organizations while they are choosing their strategies.
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Thank You