tweets classification

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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

twitter

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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