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  • Under the guidance of Prof. Poonam Pathak.

    Group Members: Vikit Shetty Hema Nair Alankrita Singh


  • IntroductionProblem Statement Literature Survey Proposed SystemAlgorithmBlock DiagramUse Case DiagramActivity Diagram Advantages Applications ConclusionReferencesIndex

  • Sentiment analysis refers to the use ofnatural language processing to identify and extract subjective information from source materials.Basic task in sentiment analysis is classifying thepolarityof a given text

    The evolution of social media has transformed the way companies view their customers. Our aim is to harvest data from reviews, rants and social feeds and subject this information to detailed sentiment analysis.


  • PROBLEM STATEMENTGiven a message, decide whether the message is of positive, negative, or neutral sentiment. For messages conveying both a positive and negative sentiment, whichever is the stronger sentiment should be chosen

  • Mining for entity opinions in Twitter, Batra and Rao used a dataset of tweets spanning two months starting from June 2009. The dataset has roughly 60 million tweets. The entity was extracted using the Stanford NER, user tags and URLs were used to augment the entities found.

    Pak and Paroubek have classifIed the tweets as objective, positive and negative. In order to collect a corpus of objective posts, they retrieved text messages from Twitter accounts of popular newspapers and magazine, such as New York Times, Washington Posts etc. Their classifier is based on the multinomial Nave Bayes classifier that uses POS-tags as features.Literature Survey:

  • To obtain customers point of view from social medias and blogs such as twitter.Pre-processing of TweetsScoring ModelTweet Sentiment ScoringTaking Review From User but only once and increasing the polarity accordingly.

  • Nave Bayes

    OI(R)| denotes the size of the set of opinion groups and emoticons extracted from the tweet, PC denotes fraction of tweet in caps,NS denotes the count of repeated letters,NX denotes the count of exclamation marks, S (AGi) denotes score of the ith adjective group, S (VGi) denotes the score of the ith verb group, S (Ei) denotes the score of the ith emoticon Nei denotes the count of the ith emoticon.Algorithm

  • Strength TableEmoticon Verb and Adverb Strength

  • Example (tweet):@kirinv I hate revision, it's BOOOORING!!! I am totally unprepared for my exam tomorrow :( :( Things are not good...#exams

  • The pre-processing of Tweet:Fraction of tweet in caps:

    There are a total of 18 words in the sentence out of which one is in all caps. Therefore, Pc= 1/18=0.055 2 Length of repeated sequence, Ns=3 Number of Exclamation marks, Nx=3The list of Adjective Groups extracted:

    AG1=totally unprepared AG2=not good AG3=boringThe list of Verb Groups extracted:

    VG1=hateThe list of Emoticons extracted:

    E1 = :( Ne1 = 2

  • Scoring Module:

    S (AG1) = S (totally unprepared) =0.8*-0.5 == -0.4S (AG2) = S (not good) =-0.8*1= -0.8S (AG3) = S (boring) = 0.5*-0.25 = -0.125Score of Verb Group :

    S (VG1) = S (hate) = 0.5*-0.75 = 0.375Tweet Sentiment Scoring:




  • Our goal in doing so is to help ALL the companies:

    Improve or defend their brand image.Track usage patterns.Monitor the reaction to new products, offers and campaigns.Tackle potential problems and ease customer concerns.Identify new revenue streams.Advantages:

  • Businesses and organizations: interested in opinionsproduct and service benchmarkingmarket intelligence survey on a topicIndividuals: interested in others opinions whenPurchasing a productUsing a serviceTracking political topicsOther decision making tasksAds placements: Placing ads in user-generated contentPlace an ad when one praises a productPlace an ad from a competitor if one criticizes a productOpinion search: providing general search for opinionsApplications:

  • These werent the only major developments: Sentiment Analysis has lead to development of better products and good business management. This research area has provided more importance to the mass opinion instead of word-of-mouth.

    In the conclusion, it has been proved that coverage expansion is good by using automatic processes where as prior polarity assignment is credible by using manual methods.

    We there by conclude by saying Go Big or Go Home.Conclusion:

  • REFERENCESPak and P. Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010, pp.13201326.R. Parikh and M. Movassate, Sentiment Analysis of User- Generated Twitter Updates using Various Classification Techniques, CS224N Final Report, 2009