Davide Feltoni Gurini, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti

Department of Computer Science and AutomationArtificial Intelligence Laboratory,

Roma Tre UniversityVia della Vasca Navale, 79, 00146 Rome, Italy

A Sentiment-Based Approach to Twitter User Recommendation

RSWEB 2013 – Hong Kong, 13 Oct 2013

Twitter - @davide_feltoni

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

A Sentiment-Based Approach to Twitter User Recommendation

Social Network: Twitter

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 3

• Free data rich of text, multimedia contents and social relationships• " Followers and " and "followees"• Relationships are mainly formed by users that share similar interests

A Sentiment-Based Approach to Twitter User Recommendation

User Profiling

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 4

Bag of Words -> KeywordsBag of Concepts -> Concepts

Metadata used to categorize topic of the tweet by keyword Hashtag #

Named-entities Persons, locations, companies, products, ..

Events Tv-shows, events with a great deal of media attention

Concepts

A Sentiment-Based Approach to Twitter User Recommendation

Motivations

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 5

User 1

PosNegNeu

User 3

PosNegNeu

Syria Sentiment Analysis

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?

User 2

PosNegNeu

A Sentiment-Based Approach to Twitter User Recommendation

Sentiment Analysis

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 6

A Sentiment-Based Approach to Twitter User Recommendation

Research Question Can implicit sentiment analysis improve user recommendation?

SVO weighting schema

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 7

Similarity Function

A Sentiment-Based Approach to Twitter User Recommendation

Dataset

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 8

31st Jan 2013 1st Mar 2013

1080500 tweets25715 users> 30000 tweets per day

A Sentiment-Based Approach to Twitter User Recommendation

Evaluation

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 9

A follow(A,B)

follow(B,A)

B

S@10: mean probability that a relevant user is in top-k positionMAP@10: average of precision value for each of the top-k recommended usersMRR: average position of a relevant user in the recommended list

Evaluation Dataset•1000 user that wrote > 50 tweet• 805.956 tweets

Mini-batch gradient descent for parameters α β and γ that maximize the performance

A Sentiment-Based Approach to Twitter User Recommendation

Experimental Results

5th ACM RecSys Workshop on Recommender Systems and the Social Web, 13 Oct 2013, Hong Kong 10

Best Parameters Achieved

A Sentiment-Based Approach to Twitter User Recommendation

J. Hannon, K. McCarthy, and B. Smyth. Finding useful users on twitter: twittomender the followee recommender.

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

A Sentiment-Based Approach to Twitter User Recommendation

RSWEB 2013 – Hong Kong, 13 Oct 2013

THANK YOU FOR YOUR ATTENTION