1. Click to Add Title A Systematic Framework for Sentiment Identification by Modeling User Social Effects Kunpeng Zhang Assistant Professor Department of Information and Decision Sciences University of Illinois at Chicago kzhang6@uic.edu

2. A World-Class Education, A World-Class City Agenda Introduction Problem statement Methodology Experiments and results Conclusion and future work

3. A World-Class Education, A World-Class City Co-authors Yi Yang, Ph.D. student at Northwestern University Aaron Sun, Research Scientist, Samsung Research America Hengchang Liu, Assistant Professor at University of Science and Technology of China

4. A World-Class Education, A World-Class City Introduction User generated content on social media platforms Data analysis for intelligent marketing decisions Voice of consumers –Positive / negative aspects

5. A World-Class Education, A World-Class City Problem Statement Given a sentence (usually, it is user-generated content on social media platforms, such as comments on Facebook, tweets on Twitter, review on Amazon.com, etc.), we classify it into one of three categories: – Positive: directly or indirectly praise something, e.g. “I love it! (^_^)” – Negative: directly or indirectly criticize something, e.g. “We don’t like it at all.  ” – Objective: No sentiments, or express a fact. e.g. “Apple will release a new iPhone in next two months.”

6. A World-Class Education, A World-Class City Previous Work Bag-of-word approaches –Collecting keywords [5, 7, 21, 26] Rule-based methods –From the perspective of language characteristics [6, 22] Machine learning based methods –Sentence-level and document-level [7, 8, 10, 29] However, –None of them considers user social effects…

7. A World-Class Education, A World-Class City Methodology Systematic framework Classification problem 4 major features: –Peer influence –User preference –User profile –Textual sentiment

8. A World-Class Education, A World-Class City Methodology 1 – User Preference (UserPref) User preference can somehow reflects user sentiments. Item-based collaborative filtering on user-item matrix –Row: user (millions) –Column: brand (thousands) –The element m ij is 1 if user i “likes” brand j, otherwise 0 m 11, m 12, …………, m 1n m 21, m 22, …………, m 2n …………… m m1, m m2, ……….., m mn Note: “like” – like a brand on Facebook, following a brand on Twitter, give a high rating for a product on Amazon, etc.

9. A World-Class Education, A World-Class City Methodology 1 – User Preference (UserPref) Two important issues using collaborative filtering –Data sparsity Integrate multiple low-lever items into fewer high-lever items –“Mac” and “iPhone”  “Computer and Electronics” –Similarity calculation and preference prediction Which similarity measure is better? –Cosine, Pearson correlation, Tanimoto correlation,log-likelihood based, Euclidean distance-based. Weighted sum strategy to approximate user preference

10. A World-Class Education, A World-Class City Methodology 2 – Peer Influence (PeerInf) Herding behavior in social psychology. –We assume that if most of previous comments in one discussion are positive, it is likely to give a positive comment, and similarly for the negative case. –We randomly pick 1, 000 posts from 5 different Facebook pages and 1, 000 discussion threads from 5 different airlines on the Flyertalk.com forum. The average number of comments per post and per thread is 794 and 32, respectively. –The sentiments are identified by the state-of-the-art textual algorithm.

11. A World-Class Education, A World-Class City Methodology 2 – Peer Influence

12. A World-Class Education, A World-Class City Methodology 2 – Peer Influence Modeling

13. A World-Class Education, A World-Class City Methodology 3 – User Profile (GenCat) Female are more positive than male and fashion page has a higher percentage of positive sentiments than politician page on Facebook and Twitter. Name (Topic)GenderPositive ratioNumber of comments + tweets Barack Obama (Politician) M0.616,837,096 F0.69 Chicago Bulls (Sports) M0.68462,092 F0.79 DKNY (Fashion)M0.9414,284 F0.96

14. A World-Class Education, A World-Class City Methodology 4 – Textual Sentiment (TextSent) State-of-the-art textual sentiment identification algorithm Ensemble method integrating three individual algorithms –Semantic rules based on language characteristics –Numeric strength computing –Bag-of-word Accuracy: ~86%

15. A World-Class Education, A World-Class City Experiments and Results Data collection –Facebook: posts, comments, likes, user profile –Twitter: tweets, follower, user profile –Amazon: product and reviews –Flyertalk (airline discussion forum): discussions Data cleaning –Remove spam users

16. A World-Class Education, A World-Class City Experiments and Results The features of learning model for 4 datasets and their differences. Topic is modified based on the raw Facebook category. “×”: missed; “√”: existing. Data sourceTextSentUserPrefPeerInfGenCat Gender Topic FacebookCommentsUser-post likes on category√√Predefined category TwitterTweetsUser-category following√√Predefined category AmazonProduct reviewsUser-product rating√×Product category FlyertalkAirline discussions×√×Airline types

17. A World-Class Education, A World-Class City Experiments and Results Similarity measure check. –MAE and RMSE to compare the average estimated error between real preference and predicted preference Hadoop-based collaborative filtering implemented by Mahout. –Takes 34 and 21 minutes to approximate user preferences for Facebook and Twitter –Can NOT complete in 10 hours for single CPU.

18. A World-Class Education, A World-Class City Experiments and Results Facebook data Twitter data Amazon.com data

19. A World-Class Education, A World-Class City Experiments and Results Classification accuracy (SS: semantic + syntactic features used in [28])

20. A World-Class Education, A World-Class City Conclusion and Future Work We propose a systematic framework to identify social media sentiments by modeling user social effects: user preference, peer influence, user profile, and textual sentiment itself. However, –More networked data could be incorporated. –More efficient algorithms to calculate user preference.

21. A World-Class Education, A World-Class City Thank you