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Twitter sentiment-analysis Jiit2013-14

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Twitter sentiment-analysis Jiit2013-14

  1. 1. TWEEZER (Twitter Sentimental Analysis) Major Project Presentation Piyush Aggarwal Rachit Goel 9910103445 9910103566 Department of CSE/IT
  2. 2. 1. Problem Statement 2. Introduction 3. Data Collection 4. Data Pre-Processing 5. Classification of Tweets 6. Working 7. Future Scope Content
  3. 3.  A major benefit of social media is that we can see the good and bad things people say about the particular brand or personality.  The bigger your company gets difficult it becomes to keep a handle on how everyone feels about your brand. For large companies with thousands of daily mentions on social media, news sites and blogs, it’s extremely difficult to do this manually.  To combat this problem, sentimental analysis software are necessary. These soft wares can be used to evaluate the people's sentiment about particular brand or personality. Problem Statement
  4. 4.  TWEEZER = TWEEts + analyZER  This product (Tweezer) introduce a novel approach for automatically classifying the sentiment of Twitter messages. These messages are classified as positive or neutral or negative with respect to a query term or the keyword entered by a user. Introduction: What is Tweezer!!
  5. 5. 1. Data Streaming: For performing sentimental analysis we need Twitter data consisting of tweets about a particular keyword or query term. For collecting the data and tweets we have used Twitter public API available for general public for free. It is the part of Data Collection. #NOTE: Tweets are short messages, restricted to 140 characters in length. Due to the nature of this micro blogging service (quick and short messages), people use acronyms, make spelling mistakes, use emoticons and other characters that express special meanings. Data Collection
  6. 6. It is a process to remove the unwanted words from tweets that does not amount to any sentiments. 1. Emotional Icons- 170 emoticons; identified emotional icons and remove them. 2. URLs-does not signify any sentiment; replaced it with a word |URL| Data Pre Processing
  7. 7. 3. Stop words- words as “a‟, “is”, “the”; does not indicate any sentiment 4. UserNames and HashTags- @ symbol before the username and # for topic; both replaced with AT_USER. Data Pre Processing(cont..)
  8. 8. 5. Repeated Letters- huuuungry, huuuuuuungry, huuuuuuuuuungry into the token “huungry". 6. Slag Words- Non English words Data Pre Processing(cont..)
  9. 9. Different Ways of Classifications-  Binary Classification: It is a two way categorization i.e. positive or negative.  3-Tier: In this, Tweets are categorized as Positive, Negative and Neutral.  5-Tier: Tweets are bucketed in 5 Classes namely: Extremely Positive, Positive, Neutral, Negative and Extremely Neutral. Characterization of Tweets
  10. 10. Sarcasm types related to twitter are as follows:  Positive words with negative smiley.  Negative words with positive smiley.  Sarcasm related to facts which includes spoofs, sarcastic recreation etc. Sarcasm Detection
  11. 11. 1. Data Pre-Processing using more parameters to get best sentiments 2. Updating Dictionary for new Synonym and Antonyms of already existing words. 3. Web-Application can be converted to Mobile Application 4. Context Sentimental Analysis may be implemented in future for accuracy purposes. Future Scope
  12. 12. Complete Model
  13. 13. Thank You

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