1. AN EFFECTIVE STATISTICAL APPROACH TO BLOG POST OPINION RETRIEVAL Ben He Craig Macdonald Iadh Ounis University of Glasgow Jiyin He University of Amsterdam CIKM 2008 1

2. Introduction  Finding opinionated blog posts is still an open problem.  A popular solution is to utilize the external resources and manual efforts in identifying subjective features.  The authors proposed a dictionary-based statistical approach, which automatically derives evidence for subjectivity from the blog collection itself, without requiring any manual effort. 2

3. TREC Opinion Finding Task (1/2)  Text REtrieval Conference.  Goal: To identify sentiment at the document-level.  The dataset are composed of:  Feed documents: XML format, usually a short summary of the blog post.  Permalink documents: HTML format, the complete blog post and its comments.  Homepage documents: HTML format, main entry to the blog. 3

4. TREC Opinion Finding Task (2/2)  Sample query format: 863 netflix Identify documents that show customer opinions of Netflix. A relevant document will indicate subscriber satisfaction with Netflix. Opinions about the Netflix DVD allocation system, promptness or delay in mailings are relevant. Indications of having been or intent to become a Netflix subscriber that do not state an opinion are not relevant. 4

5. Statistical Dictionary-based Approach Dictionary Generation Term Weighting Generating the Opinion Score Score Combination 5

6. Dictionary Generation  The Skewed Query Model  Rank all terms in the collection by term frequencies in descending order.  The terms, whose rankings are in the range (S·#terms, U·#terms) are selected in the dictionary. #terms : the number of unique terms in the collection S,U : model parameters. S=0.00007 and U=0.001 in this paper. 6

7. Dictionary Generation Ex: #terms=200,000 #terms x 0.00007=14 #terms x 0.001=200 Only those terms ranked 14 to 200 will be preserved  The dictionary is not necessary opinionated. 7

8. Term Weighting (1/2)  KL divergence method  D(Rel): Collection of relevant documents. D(opRel): Collection of opinionated and relevant documents. c(D(opRel))= #tokens in the opinionated documents. c(D(Rel))= #tokens in the relevant documents. tf x =term frequency of the term t in the opinionated documents. tf rel =term frequency of the term t in the relevant documents. 8

9. Term Weighting (2/2)  Bose-Einstein statistics method  Measures how informative a term is in the set D(opRel) against D(Rel).  = : the frequency of the term t in the D(Rel). : the number of documents in D(Rel). : the frequency of the term t in the D(opRel). 9

10. Generating the Opinion Score  Take the X top weighted terms from the opinion dictionary.  X will be tuned in the training step.  Submit them to the retrieval system as a query Q opn.  Score(d,Q opn ): the opinion score of document d.  Score(d,Q): the initial ranking score. 10

11. Score Combination  Linear combination:   Log combination:   a, k will be tuned in the training step. 11

12. Experiment Settings (1/3)  TREC06: 50 topics for training.  TREC07: 50 topics for testing.  Only the “title” field is used (1.74 words/topic).  Baseline 1: Apply InLB model, a variation of the BM25 ranking function. Retrieve as many relevant documents as possible.  12

13. Experiment Settings (2/3)  Baseline 2: favor documents where the query terms appear in close proximity.  Q 2 : The set of all query term pairs in query Q. N: #Docs in the collection. T: #Tokens in the collection. pfn: The normalized frequency of the tuple p. 13

14. Experiment Settings (3/3)  Manually collecting an external dictionary from OpinionFinder and several other resources.  Contains approximately 12,000 English words, mostly adjectives, adverbs and nouns. 14

15. Experiment: Term Weighting (1/2)  Hypothesis: the most opinionated terms for one query set are also good indicators of opinion for other queries.  Sampling:  For each sample set, calculate the weight of each terms. Training Set (50 Topics) Set 1 Set 2 Set 10 … Each with 25 Topics Overlap : 65% maximum 15

16. Experiment: Term Weighting (2/2)  Compute the cosine similarity between the weights of the top 100 weighted terms from each two samples 16

17. Experiment: Validation (1/3)  Tuning the parameters X, a and k mentioned before.before  Maximize X by maximizing the mean MAP of the 10 samples. 17 Training Set (50 Topics) Set1 for assigning term weight Set1’ for validation

18. Experiment: Validation (2/3) 18

19. Experiment: Validation (3/3)  Fix X=100, tuning a and k.  a within [0, 1], step=0.05  k within (0, 1000], step=50 19

20. Experiment: Evaluation (1/3) 20

21. Experiment: Evaluation (2/3) 21

22. Experiment: Evaluation (3/3)  Comparison with the OpinionFinder  All being equal, replace the opinion score Score(d,Q opn ) with 22

23. Conclusion  An effective and practical approach to retrieving opinionated blog posts without manual effort.  Opinion scores are computed during indexing  Computational cost is negligible.  The automatically generated internal dictionary performs as good as the external dictionary.  Diferrent random samples from the collection reach a high consensus on the opinionated terms if the Bose-Einstein statistics given by the geometric distribution are applied. 23

24. Thank you for listening! 24