A Sentiment-Based Approach to Twitter User Recommendation

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A Sentiment-Based Approach to Twitter User Recommendation

  1. 1. A Sentiment-Based Approach to Twitter User Recommendation Davide Feltoni Gurini, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti Department of Computer Science and Automation Artificial Intelligence Laboratory, Roma Tre University Via della Vasca Navale, 79, 00146 Rome, Italy Twitter - @davide_feltoni RSWEB 2013 – Hong Kong, 13 Oct 2013
  2. 2. A Sentiment-Based Approach to Twitter User Recommendation Outline • • • • Introduction and Motivations SVO Weighting Schema Dataset and Evaluation Results Conclusions and Future Works 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 2
  3. 3. A Sentiment-Based Approach to Twitter User Recommendation Social Network: Twitter • Free data rich of text, multimedia contents and social relationships • " Followers and " and "followees" • Relationships are mainly formed by users that share similar interests 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 3
  4. 4. A Sentiment-Based Approach to Twitter User Recommendation User Profiling Bag of Words -> Keywords Bag of Concepts -> Concepts Concepts Hashtag # Named-entities Events Metadata used to categorize topic of the tweet by keyword Persons, locations, companies, products, .. Tv-shows, events with a great deal of media attention 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 4
  5. 5. A Sentiment-Based Approach to Twitter User Recommendation Motivations User 1 N°tweets = 93 #Politics, #Syria, .. Democratic? User 2 N°tweets = 84 #Politics, #Syria, .. CNN, BBC, .. User 3 N°tweets = 89 #Politics, #Syria, .. Republican? Syria Sentiment Analysis User 1 User 2 User 3 Pos Pos Pos Neg Neg Neg Neu Neu Neu 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 5
  6. 6. A Sentiment-Based Approach to Twitter User Recommendation Sentiment Analysis Research Question Can implicit sentiment analysis improve user recommendation? 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 6
  7. 7. A Sentiment-Based Approach to Twitter User Recommendation SVO weighting schema Similarity Function 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 7
  8. 8. A Sentiment-Based Approach to Twitter User Recommendation Dataset 1080500 tweets 25715 users > 30000 tweets per day 31st Jan 2013 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 1st Mar 2013 8
  9. 9. A Sentiment-Based Approach to Twitter User Recommendation Evaluation follow(B,A) A follow(A,B) Evaluation Dataset B •1000 user that wrote > 50 tweet • 805.956 tweets Mini-batch gradient descent for parameters α β and γ that maximize the performance S@10: mean probability that a relevant user is in top-k position MAP@10: average of precision value for each of the top-k recommended users MRR: average position of a relevant user in the recommended list 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 9
  10. 10. A Sentiment-Based Approach to Twitter User Recommendation Experimental Results Best Parameters Achieved J. Hannon, K. McCarthy, and B. Smyth. Finding useful users on twitter: twittomender the followee recommender. 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 10
  11. 11. A Sentiment-Based Approach to Twitter User Recommendation Conclusions and Future Works • Richer weighting schema compared with " state-of-the-art " • Implicit sentiment analysis to improve recommendation • Preliminary evaluation shows the benefits of the proposed approach • • • • Use a general dataset (Hannon et al.) Expand concepts to Named Entities, Products, Events, … Improve recommendation leveraging Collaborative Filtering Sensitivity Analysis for parameters 5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 11
  12. 12. THANK YOU FOR YOUR ATTENTION RSWEB 2013 – Hong Kong, 13 Oct 2013

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