Identifying Comparative Sentences in Text Documents

1. Identifying Comparative Sentences in Text Documents Nitin Jindal and Bing Liu University of Illinois SIGIR 2006

2. Introduction • Comparisons are one of the most convincing ways of evaluation. • Much of such info is available on the Web (customer reviews), forum discussions, and blogs. • Useful for product manufacturers and potential customers (to make purchasing decisions).

3. Comparisons vs. Opinions • Comparisons can be both objective or subjective. • Comparative sentences have different language constructs from typical opinion sentences. • Comparative sentences may contain some indicators. Car X is much better than Car Y Car X is two feet longer than Car Y

4. Related Work • Linguistics: based on grammars (syntax and semantics) and logic (gradability), which is more for human consumption than for automatic identification. • Opinion tasks: opinion extraction and classification problem, which is quite different from this comparison identification.

5. Comparatives (Linguistic) • Comparatives are used to express explicit orderings between objects with respect to the degree or amount to which they possess some gradable property. John is taller than he was => John is tall to degree d

6. Comparatives (Linguistic) • Two broad types: • Metalinguistic Comparatives: compare properties of one entity. Ronaldo is angrier than upset. • Propositional Comparatives: compare between two propositions. Three subcategories:

7. Comparatives (Propositional) • Nominal Comparatives: (two sets of entities) Paul ate more grapes than bananas. • Adjectival Comparatives: (than, as good as) Ford is cheaper than Volvo. • Adverbial Comparatives: (occur after a verb phrase) Tom ate more quickly than Jane.

8. Superlatives • Adjectival Superlatives: John is the tallest person. • Adverbial Superlatives: Jill did her homework most frequently. • Equality: conjunctions like and, or, … John and Sue, both like sushi.

9. POS involved • NN: Noun • NNP: Proper Noun • VBZ: Verb, present tense, 3rd person singular • JJ: Adjective • RB: Adverb • JJR Adjective, comparatives • JJS: Adjective, superlative • RBR: Adverb, comparative • RBS: Adverb, superlative

10. Limitations of linguistic classification. • Non-comparatives with comparative words: many non-comparatives contain comparative words. In the context of speed, faster means better. John has to try his best to win this game. • Limited coverage: many comparatives contain no comparative words. In market capital, Intel is way ahead of Amd. Nokia Samsung, both cell phones perform badly on heat dissipation index. The M7500 earned a World bench score of 85, whereas Asus A3V posted a mark of 89.

11. Enhancements • First limitation: machine learning methods to distinguish comparatives and non-comparatives. • Second limitation: • User preferences: I prefer Intel to Amd = Intel is better than Amd • Implicit comparatives: Camera X has 2 MP, whereas camera Y has 5 MP.

12. Types of Comparatives • Non-Equal Gradable: greater or less than type, including user preferences. • Equative (Gradable): equal to type • Superlative (Gradable): greater of less than all others type • Non-Gradable: • A is similar to B; A has feature F1 while B has F2; A has feature F but B doesn’t

13. Tasks • Identifying comparative sentences from a given text data set. • Extracting comparative relations from sentences. (Mining comparative sentences and relations, AAAI 2006)

14. Class Sequential Rules with Multiple Minimum Supports • For sequential pattern mining, patterns to the left and class to the right. • Select patterns: keywords – POS (JJR, RBR, JJS, RBS) + Words (favor, prefer, win beat, but…) + Phrases (number one, up against) • The performance of only using keywords are P=32%, R=94%.

15. Support and Confidence • Using the minimum support of 20% and minimum confidence of 40%, one of the discovered CSRs is:

16. Building the Sequence DB this/DT camera/NN has/VBZ significantly/RB more/JJR noise/NN at/IN iso/NN 100/CD than/IN the/DT nikon/NN 4500/CD {NN}{VBZ}{RB}{moreJJR}{NN}{IN}{NN} -> comparative • Sequences which exceeds 60% confidence threshold become rules. Minimum support = 10%. • 13 Manual rules with conjunctions as whereas/IN, but/CC, however/RB, while/IN, though/IN, although/IN, etc..

17. Classification Learning • Machine learning methods: Feature Set = {X | X is the sequential pattern in CSR X → y} ∪ {Z | Z is the pattern in a manual rule Z → y}

18. Data Preparation • Consumer reviews on products such as digital cameras, DBD players, MP3 players and cellular phones. • Forum discussions on topics such as Intel vs. AMD, Coke vs. Pepsi, and Microsoft vs. Google. • News articles on topics such as automobiles, ipods, and soccer vs. football.

19. Number of Sentences in Data Sets

20. Experimental Results (1)

21. Experimental Results (2) • Review: R low P high -> short sentences, hard to find patterns • Articles and Forums: R high P low -> long sentences and find patterns too easily or find too many patterns.

22. Conclusion and Future Work • Identifying comparative sentences. • Analyzing different types of comparative sentences. • Studying how to automatically classify subjective and objective comparisons.