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. A Sentiment-Based Approach to Twitter User
Recommendation
Outline
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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12. THANK YOU FOR YOUR
ATTENTION
RSWEB 2013 – Hong Kong, 13 Oct 2013