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Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
Twitter Mining & Sentiment Analysis
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Twitter Mining & Sentiment Analysis

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Using Twitter for marketing purpose. We have collected tweets from twitter over the Thanks Giving '13 weekend. And performed Text Mining and Sentiment Analysis on the tweets in an attempt to …

Using Twitter for marketing purpose. We have collected tweets from twitter over the Thanks Giving '13 weekend. And performed Text Mining and Sentiment Analysis on the tweets in an attempt to understand how customers reacted to various deals and promotions.

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  • 1. By Datla Aditya Koya SivaProf Dr. Ramesh Sharda Pappoppula Veerendra
  • 2. • Social media analytics• Focusing on customer views• Targeting the Black Friday andCyber Monday season to getthe customer’s views on salesand deals.
  • 3.  Over $59 Billion generated over the Black Friday weekend. Predicting consumer behavior is crucial to maximize revenues. To deliver a consistent, positive customer experience.
  • 4. Demonstration
  • 5. Retailers:Products : Blu-Ray Player, Camera, Tablet, JewelryBrands :Strange : Bill Gates
  • 6.  Walmart: Bestbuy, fires, savemoney, Target, handgun Target: Bestbuy, books, Walmart, pjs, Kindle, Galaxynoteii Ebay: Apple, clearance, streetwear, mobile, shirt Amazon: giftcards, giftideas, Cybermonday, DVD, Bluray Billgates: spread, givingtuesday, Blackfriday, sale, sell
  • 7.  Bill Gates, Black Friday, Cyber Monday, Deals.
  • 8.  Sentiment analysis or opinion mining. Sentiment Score is calculated. The opinion lexicon made by Hu and Liu . Score = no. of positive words – no. of negative words.
  • 9. 900 600 450
  • 10. 300 -100
  • 11. 150 650800 600
  • 12.  Retailer with most positive response this Thanksgiving sale: 900 Brand with most positive response this Thanksgiving sale: 800
  • 13. Thank You

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