Understanding User Intentions by Computational Techniques Hongning Wang

Understanding User Intentions by Computational Techniques Hongning Wang Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL, 61801 USA

Research Summary Great hotel = price<$60, location in downtown Good MP 3 player = large memory, long batter life Relevant news = most recent report § Latent Aspect Rating Analysis [KDD’ 10, 11] § Online Forum Discussion Structure Modeling [SIGIR’ 11] § Latent Topical Structure Modeling [ACL’ 11] 2/23/2021 2

Information buried in text content 2/23/2021 3

Latent Aspect Rating Analysis [KDD’ 10, 11] Entity Aspects Location location amazing walk anywhere Room room dirty appointed smelly Service terrible front-desk smile unhelpful 2/23/2021 Review Aspect Rating Aspect Weight Excellent location in walking distance to Tiananmen Square and shopping streets. That’s the best part of this hotel! The rooms are getting really old. Bathroom was nasty. The fixtures were falling off, lots of cracks and everything looked dirty. I don’t think it worth the price. Service was the most disappointing part, especially the door men. this is not how you treat guests, this is not hospitality. 0. 86 0. 04 0. 10 4

LARA Applications I • User rating emphasis analysis City Avg. Price Amsterdam 241. 6 San Francisco 261. 3 Florence 272. 1 Group Val/Loc Val/Rm Val/Ser top-10 190. 7 214. 9 221. 1 bot-10 270. 8 333. 9 236. 2 top-10 214. 5 249. 0 225. 3 bot-10 321. 1 311. 4 top-10 269. 4 248. 9 220. 3 bot-10 298. 9 293. 4 292. 6 – Reviewers emphasize ‘value’ aspect would prefer ‘cheap’ hotels 2/23/2021 5

LARA Applications II • User rating behavior analysis Expensive Hotel Cheap Hotel 5 Stars 3 Stars 5 Stars 1 Star Value 0. 134 0. 148 0. 171 0. 093 Room 0. 098 0. 162 0. 126 0. 121 Location 0. 171 0. 074 0. 161 0. 082 Cleanliness 0. 081 0. 163 0. 116 0. 294 Service 0. 251 0. 101 0. 049 – Reviewers focus differently on ‘expensive’ and ‘cheap’ hotels 6

Oops, time is limited. . . Online Forum Discussion Structure Modeling [SIGIR’ 11] Latent Topical Structure Modeling [ACL’ 11] • Probabilistic model with rich features • Topical transition structure Initial topic Content topic proportion 2/23/2021 Transition probability Emission probability 7

Future Direction • Mining rich user-generated-data – Clicks, sharing, like • Analyzing social interactions – Friendship, following 2/23/2021 8

Thank you! • Q&A 2/23/2021 9

Information Hidden in Structures • Structure is not always visible Flat View Threaded View v. s. 2/23/2021 10

Online Forum Discussion Structure Modeling 2/23/2021 11

Online Forum Discussion Structure Modeling • Probabilistic model with rich features – Post attributes – User interactions 2/23/2021 12

Recognizing and modeling document structure • Languages are intrinsically cohesive and coherent 2/23/2021 13

Latent Topical Structure Modeling Initial topic Content topic proportion 2/23/2021 Transition probability Emission probability 14

LARA Applications I • Corpus specific word sentimental orientation Value Rooms Location Cleanliness resort 22. 80 view 28. 05 restaurant 24. 47 clean 55. 35 value 19. 64 comfortable 23. 15 walk 18. 89 smell 14. 38 excellent 19. 54 modern 15. 82 bus 14. 32 linen 14. 25 worth 19. 20 quiet 15. 37 beach 14. 11 maintain 13. 51 bad -24. 09 carpet -9. 88 wall -11. 70 smelly -0. 53 money -11. 02 smell -8. 83 bad -5. 40 urine -0. 43 terrible -10. 01 dirty -7. 85 road -2. 90 filthy -0. 42 overprice -9. 06 stain -5. 85 website -1. 67 dingy -0. 38 ▫ Uncover sentimental information directly from the data 15

Learned Topical Structure 2/23/2021 16