Topic Estimation for Microblogs Taking into Account the Relationships between Adjacent Tweets

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1 1,a) LDA 1 1 Topic Estimation for Microblogs Taking into Account the Relationships between Adjacent Tweets Naoya Nakamura 1,a) Ryohei Sasano 2 Hiroya Takamura 2 Manabu Okumura 2 Abstract: In recent years, micloblogs have appeared and become prevalent all over the world. Since the characteristics of the information contained in micloblogs are considered to be different from those contained in conventional media, we can get a lot of useful information from micloblogs. However, commonly used topic models, such as LDA, do not work well on micloblogs, since these topic models do not take into account the relationships between adjacent posts and the shortness of each post. In this paper, we propose a new topic model for micloblogs that takes into account the relationships between adjacent posts. Keywords: Information extraction, Microblog, Topic model Twitter Twitter 1 Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology 2 Precision and Intelligence Laboratory, Tokyo Institute of Technology a) nakamura0617@lr.pi.titech.ac.jp [7] Twitter [4] [8] Twitter Twitter LDA Twitter 1

Since the characteristics of the information contained in micloblogs are considered to be different from those contained in conventional media, we can get a lot of useful information from micloblogs.

2 Latent Dirichlet Allocation 1 1 LDA 1 1 Zhao 1 Twitter 1 1 Twitter-LDA [10] 1 Zhao Twitter Twitter-LDA Latent Dirichlet Allocation Blei Latent Dirichlet Allocation (LDA) [1] LDA Twitter-LDA LDA Twitter 1 LDA Twitter-LDA [10] 2 ( 1 ) 1 ( 2 ) 1 1 (1) (2) 1 1 Twitter-LDA T U φ={φ 1,...,φ T } - θ={θ 1,...,θ U } - z={z 1,...,z Nu } α β φ t θ u θ u φ z s z s s 2.3 Titov Multi-grain LDA (MG-LDA) [9] Gruber Hidden Topic Markov Model (HTMM) 2

2 Twitter-LDA LDA Twitter 1 LDA Twitter-LDA [10] 2 ( 1 ) 1 ( 2 ) 1 1 (1) (2) 1 1 Twitter-LDA T U φ={φ 1,.

3 [3]Diao Twitter TimeUserLDA [2] Twitter Twitter ACL ( 1 ) ( 2 ) ACL ( 3 ) ( 1 ) ( 2 ) ACL ( 3 ) Twitter-LDA θ u 2 1 θ u Twitter-LDA 2 3 y Twitter-LDA ( 1 ) for each topic t = 1,..., T draw φ t Dir(β) ( 2 ) for each user u = 1,..., U ( a ) draw θ u Dir(α) ( b ) draw z 1 Multinomial(θ u ) ( c ) for each tweet s = 2,..., N u ( i ) draw y s Bernoulli(γ) ( ii ) if y s = 0 then draw z s Multinomial(θ u ) else z s z s 1 ( iii )for each word i = 1,..., N u,s draw w i Multinomial(φ z s ) (2)(b) Twitter-LDA (2)(c) 3.3. LDA d z d T N d T N d d T Twitter-LDA 1 1 3

.., U ( a ) draw θ u Dir(α) ( b ) draw z 1 Multinomial(θ u ) ( c ) for each tweet s = 2,.

4 2 Twitter-LDA n n+1 n+1 2 ( 1 ) M ( 2 ) M p(z i j z i, S i, y i ) p(s ij z i j )p(z i j ) 1 V sij 1 (n( ) k=0 t + Vβ k) v=1 Γ(n (v) t + β) Γ(n (v) t, S i + β) (nt u + α) n (v) t t v n ( ) t t n t u u t S s ij i j 4 5 S i i z i i y i i 5 5 M = Twitter URL 4

5 1 1 2,388, ,517 1,073 topic coherence 120 γ γ = 0.0 Twitter-LDA α = 0.01 β = 0.01 T M M = 10 perplexity γ 6 perplexity 4.2 perplexity perplexity ( ) perplexity perplexity = exp 1 log p(s u ) N s u s u N t p(s) p(s) = p(s, z) z = p(z 1 )p(s 1 z 1 ) p(z j z j 1 )p(s j z j ) z j=2 = p(z 1 )p(s 1 z 1 )p(s 2,..., s s z 1 ) z γ perplexity 6 γ perplexity γ = 0.0 Twitter-LDA γ = 0.0 γ = 0.0 γ perplexity γ = 0.8 perplexity perplexity perplexity topic coherence[5], [6] topic coherence topic coherence C(t; V (t) ) = K k 1 k=2 l=1 log D(v(t) k v(t) l ) + 1 D(v (t) l ) D(v (t) ) t v D(u (t), v (t) ) t u v K K t V (t) γ topic coherence 7 γ topic coherence γ = 0.0 γ γ = 0.6 topic coherence γ = 0.6 γ topic coherence Twitter-LDA 5

2 perplexity perplexity ( ) perplexity perplexity = exp 1 log p(s u ) N s u s u N t p(s) p(s) = p(s, z) z = p(z 1 )p(s 1 z 1 ) p(z j z j 1 )p(s j z j ) z j=2 = p(z 1 )p(s 1 z 1 )p(s 2,.

6 topic coherence γ 7 topic coherence 5. 5 Leenders, and Andrew McCallum. Optimizing semantic coherence in topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 11, pages , Stroudsburg, PA, USA, Association for Computational Linguistics. [6] David Newman, Youn Noh, Edmund Talley, Sarvnaz Karimi, and Timothy Baldwin. Evaluating topic models for digital libraries. In Proceedings of the 10th annual joint conference on Digital libraries, JCDL 10, pages , New York, NY, USA, ACM. [7] Owen Phelan, Kevin McCarthy, and Barry Smyth. Using twitter to recommend real-time topical news. In Proceedings of the third ACM conference on Recommender systems, Rec- Sys 09, pages , New York, NY, USA, ACM. [8] Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, WWW 10, pages , New York, NY, USA, ACM. [9] Ivan Titov and Ryan McDonald. Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW 08, pages , New York, NY, USA, ACM. [10] Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee- Peng Lim, Hongfei Yan, and Xiaoming Li. Comparing twitter and traditional media using topic models. In Proceedings of the 33rd European conference on Advances in information retrieval, ECIR 11, pages , Berlin, Heidelberg, Springer-Verlag. [1] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3: , March [2] Qiming Diao, Jing Jiang, Feida Zhu, and Ee-Peng Lim. Finding bursty topics from microblogs. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages , Jeju Island, Korea, July Association for Computational Linguistics. [3] Amit Gruber, Michal Rosen-zvi, and Yair Weiss. Hidden topic markov models. In In Proceedings of Artificial Intelligence and Statistics, [4] Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, WWW 10, pages , New York, NY, USA, ACM. [5] David Mimno, Hanna M. Wallach, Edmund Talley, Miriam 6

[6] David Newman, Youn Noh, Edmund Talley, Sarvnaz Karimi, and Timothy Baldwin. Evaluating topic models for digital libraries.

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