1. Intelligent Database Systems Lab Presenter : JIAN-REN CHEN Authors : Ahmed Abbasi, Stephen France, Zhu Zhang, and Hsinchun Chen 2011, IEEE TKDE Selecting Attributes for Sentiment Classification Using Feature Relation Networks

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation Sentiment analysis has emerged as a method for mining opinions from such text archives. challenging problem: 1.requires the use of large quantities of linguistic features 2.integrate these heterogeneous n-gram categories into a single feature set - noise 、 redundancy and computational limitations 1)polarity 2)intensity I don’t like you 、 I hate you

4. Intelligent Database Systems Lab n-gram - (Markov model) 天氣：晴天、陰天、雨天 美麗 vs 美痢 “HAPAX” and “DIS” tags I hate Jim replaced with “I hate HAPAX”

5. Intelligent Database Systems Lab Objectives Feature Relation Network (FRN) considers semantic information and also leverages the syntactic relationships between n-gram features. - enhanced sentiment classification on extended sets of heterogeneous n-gram features.

6. Intelligent Database Systems Lab Methodology- Extended N-Gram Feature Set

7. Intelligent Database Systems Lab Methodology - Subsumption Relations A subsumes B(A → B) “I love chocolate” unigram : I, LOVE, CHOCOLATE bigrams : I LOVE, LOVE CHOCOLATE trigrams : I LOVE CHOCOLATE “I love chocolate” unigram : I, LOVE, CHOCOLATE bigrams : I LOVE, LOVE CHOCOLATE trigrams : I LOVE CHOCOLATE Ｗ hat about the bigrams and trigrams? It depends on their weight. Their weight exceeds that of their general lower order counterparts by threshold t.

8. Intelligent Database Systems Lab Methodology - Parallel Relations A parallel B (A － B) POS tag: “ADMIRE_VP” → “like” semantic class: “SYN-Affection” → “love” POS tag: “ADMIRE_VP” → “like” semantic class: “SYN-Affection” → “love” A and B have a correlation coefficient greater than some threshold p, one of the attributes is removed to avoid redundancy.

9. Intelligent Database Systems Lab Methodology - The Complete Network

10. Intelligent Database Systems Lab Methodology - Incorporating Semantic Information

11. Intelligent Database Systems Lab Experiments - Datasets

12. Intelligent Database Systems Lab Experiments – FRN vs Univariate

13. Intelligent Database Systems Lab Experiments - FRN vs Univariate (WithinOne)

14. Intelligent Database Systems Lab Experiments - FRN vs Multivariate

15. Intelligent Database Systems Lab Experiments - FRN vs Multivariate (WithinOne)

16. Intelligent Database Systems Lab Experiments - FRN vs Hybrid

17. Intelligent Database Systems Lab Experiments - FRN vs Hybrid (WithinOne)

18. Intelligent Database Systems Lab Experiments - Ablation

19. Intelligent Database Systems Lab Experiments - Parameter t (0.0005, 0.005, 0.05, and 0.5) p (0.80, 0.90, and 1.00)

20. Intelligent Database Systems Lab Experiments - Average Runtimes

21. Intelligent Database Systems Lab Conclusions FRN had significantly higher best accuracy and best percentage within-one across three testbeds. The ablation and parameter testing results play an important role for the subsumption and parallel relation thresholds.

22. Intelligent Database Systems Lab Comments Advantages - accuracy 、 computationally efficient Disadvantage - ablation and parameter is sensitive Applications - sentiment classification - feature selection method