Twitter now a days most top social networking where lots of data is generated day by day,In which people discuss with world issues and own opinion to political parties .If we process this data to analyse how much people are positive in this particular and how much are negative or how much are neutrol .In this project I did this with the help of PYTHON NLTK

1. Sentiment
Analysis
On
Twitter
Facebook
TEXT SAMPLE

2. Problem Statement
- Review Related Websites
Movies reviews ,product reviews
- Knowing the public opinions for
political leaders
- To make decisions
- Rapid growth of available
subjective text on the
internet
"what other people think” has always been an
important element of information for decision making
process.
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3. Technology and APIs Used
▹ Django
▹ REST Framework
▹ Tweepi Api
▹ Python NLTK
▹ HTML,CSS,JAVASCRIPT
▹ AJAX
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5. Demo
Of
Project 5

6. Issues In Sentiment
Analysis
Sarcasm
one of the most
difficult sentiments
for automated
tracking to
interpret properly.
Example: “It was
awesome for the week
that it worked.”
Compound or
multidimensional
sentiment
contain positives and
negatives in the
same phrase.
Example: “I love Mad
Men, but hate the
misleading episode
trailers.”
Conditional
sentiment
includes actions that
may happen in the
future.
Example:
the customer isn’t
angry now but says he
will be if the company
doesn’t call him back.
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7. THANKS!
Any questions?
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