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Tweets Classification
Supervisor - Dr. Vikas Saxena
Name - Shubhangi Agarwal
Varun Ajay Gupta
Enrolment No. – 10104768
10104730

2. 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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3. 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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4. 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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5. 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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6. Technology Used
• Operating System: UBUNTU 12.04 .
• Language: PYTHON
• Tools: GEDIT
• Debugger: PYTHON DEBUGGER
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8. Unstructured Tweets
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9. Structured Tweets
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10. Calculating most popular
language on twitter
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11. Pictorially showing
popularity of languages
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12. 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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13. Values of features of
training set
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14. Feature values of testing data
set before application of SVM
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15. Result of classification of
tweets
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16. Graph of SVM and
accuracy
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17. Conclusion
On 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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18. 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