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Apr 15, 2017
Text Extraction In Social Media
SENTIMENTAL ANALYSIS TOOLBY:-RAVINDRA CHAUDHARYSACHIN SINGH
UNDER THE GUIDENCE OFMRS. SMITA TIWARI
CONTENTIntroductionProblem StatementObjectiveTools/TechniquesMethodologyImplementationResults & DiscussionConclusionFuture Scope of the project
INTRODUCTIONWhat 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 guynegative :- jasleen is misusing the law..Neutral :- waiting for court decision..
Why using twitter for sentiment analysis:-
Social networking and microblogging website.Short text messages 140 Character.316+ million active users and 500 million tweets per day generated People share their thoughts using twitter it may be any social issue ,movie ,politics , news and so on.Also share current affairs and personal view on different topics..The challenge is to gather all such relevant data , detect and summarize the overall sentiment on a topic.
Problem StatementThe problem in the sentiment analysis is classifying the polarity of given text in a document in a sentence
Whether the expressed opinion in the document or in a sentence is positive ,negative or neutral.
ObjectiveTo implement an Algorithm(Nave Bayes algorithm) for classification to text into Positive , Negative ,or Neutral.Making more data set for more accurate results.
Nave Bayes ClassifiersIn machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s, and remains a popular (baseline) method for text categorization,
the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the featuresNaive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables
NAVE BAYES EXAMPLE:-
NET BEANS IDE 8.0WAMP SERVER MY SQLHTML5CSSJAVA
DATA COLLECTION download the tweets using Twitter 4J API.
TOKENSIER Twitter using POS(part of speech) tagger..
PRE-PROCESSING Remove slag words. Remove URL and HASTAG(#),numbers. Replace sequence of repeated character coooooool by cool. Remove noun and prepositions
FEATURE EXTRACTIONPercentage of capitalized wordNo of ve /+ve capitalized wordNo of +ve /-ve hashtagNo of +ve /-ve emoticonsNo. of negationsNo. of special characters [email protected]#%^*
CLASSIFICATION AND PREDECTIONS
The model is built to predict the sentiment of new tweetsFeature extracted are next focused to classifier
Types of ClassificationBinary classification:- only Positive , Negative .
2. 3 Teir:- Positive , Negative and Neutral .
3. 5 Teir :- :- Extremely Positive , Extremely Negative , Positive , Negative and Neutral
Future scopeWeb application can be converted to mobile applicationsSentiment analysis may be implemented in future for accuracy purposesUpdating dictionary for new synonyms and antonyms
By improving the data sets we get more accurate results (sentiments).