sentiment alalysis

1. Text Extraction In Social Media
BY:-
RAVINDRA CHAUDHARY
SACHIN SINGH
UNDER THE GUIDENCE OF
MRS. SMITA TIWARI

2. CONTENT
 Introduction
 Problem Statement
 Objective
 Methodology
 Module
 Future work and scope
 Conclusion

3. INTRODUCTION
What is Sentiment Analysis…??
 It is the classification of the polarity of given text in the document.
 The goal is to determine whether the expressed opinion in the text is
Positive,Negative or Neutral.
For Example:-
 Positive :- sarvjeet is good guy…
 negative :- jasleen is misusing the law..
 Neutral :- waiting for court decision..

4. Problem Statement
 Sentiment analysis is classifying the polarity of given text in a document in a
sentence is positive ,negative or neutral.
 To collecting data and categorizing into different sets for different purposes.
 Making sentiment tool for measuring all sentiments by one tool.
 Increase the accuracy of the result which is measured by sentiment tool.

5. Objective
 To implement naïve baye’s Algorithm for classification to text polarity into
Positive , Negative,or Neutral sentiments.
 Using different type of NLTK classifiers for classifying with more accuracy.

6. Methodology
1. DATA COLLECTION
 download the tweets using Twitter API.
2. TOKENSIER
 Twitter using POS(part of speech) tagger.
3. PRE-PROCESSING
 Remove slag(non-english) words
 Replacing emoticons by their polarity.
 Remove URL and HASTAG(#),numbers.
 Peplace sequence of repeted character coooooool by cool.
 Remove noun and prepositions.

7. FEATURE EXTRACTION
 Percentage of capitalized word
 No of –ve/+ve capatilized word
 No of +ve/-ve hastag
 No of +ve/-ve emoticons
 No. of negations
 No. of special characters ex..@#%^*
CLASSIFICATION AND PREDECTIONS
 The model is built to predict the sentiment of new tweets…
 Feature extracted are next focused to classifier

8. MODULES OF PROJECT
 Module 1:- Extracting data from social media.
 Module 2:- Tokensing fetched data.
 Module 3:- Preprocessing fetched data.
 Module 4:-: feature extraction of data.
 Module5:- Classification of data.

9. MODULE 1:- We are using facepager to extract the data from
social media. The following process shows how to fetch data
from social media

16. Future work and scope
 Web application can be converted to mobile applications
 Sentiment analysis may be implemented in futyre for accuracy
purposes
 Updating dictionary for new synonyms and antonyms
 Data pre-processing using more parameters to get best sentiments

17. Conclusion
 We conclude that using different classifiers it is easier to classify the tweets and
documents.
 By improving the data sets we get more accurate results (sentiments).

18. THANKYOU EVERYONE
For taking interest in my slides