Finding coherent topics in Twitter data is difficult task because of the sparseness and informal language. Tweets also provide rich contextual and auxiliary metadata which can be used to supervise the topic modeling to get more coherent topics. In this paper, a novel topic model is proposed which extends Author Topic Model for twitter. Standard Author Topic Model cannot be used on Twitter data as every tweet has exactly one author. The proposed User Graph Topic Model (UGTM) considers the semantic relationships among tweet users based on the contextual information like hashtags, user mentions and replies to make a user graph. Related users of author of a tweet are found and used in tweet generation process. Related user information from the user graph is used to obtain the dirichlet prior for user generation. Empirical results show that the proposed UGTM outperforms standard Author Topic Model (ATM) on experimental data.
D.M.Blei, Probabilistic topic models, Communications of the ACM55(4) (2012), 77–84.
2.
D.Wang, et al., Multi-document summarization using sentence-based topic models, In Proceedings of the ACLI-JCNLP 2009 Conference Short Papers. Association for Computational Linguistics, 2009.
3.
G.Yang, et al., A novel contextual topic model for multi-document summarization, Expert Systems with Applications42(3) (2015), 1340–1352.
4.
N.Akhtar, H.Javed and T.Ahmad, Hierarchical summarization of text documents using topic modeling and formal concept analysis, Data Management, Analytics and Innovation2 (2018), 21.
5.
N.Akhtar, Hierarchical summarization of news tweets with twitter-lda, In Applications of Soft Computing for the Web, Springer, 2017, pp. 83–98.
6.
C.Lin and Y.He, Joint sentiment/topic model for sentiment analysis, In Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM, 2009.
7.
N.Akhtar, N.Zubair, A.Kumar and T.Ahmad, Aspect based sentiment oriented summarization of hotel reviews, Procedia Computer Science115 (2017), 563–571.
8.
Z.Zhai, et al., Constrained lda for grouping product features in opinion mining, In Pacific-Asia Conference on Knowledge Discovery and Data Mining Heidelberg, Berlin, Springer, 2011.
9.
K.T.Ho, Q.V.Bui and B.Marc, Dynamic social network analysis using author-topic model, In International Conference on Innovations for Community Services, Cham, 2018.
10.
Y.Hu, K.Zhai, V.Edelman and J.Boyd-Graber, Polylingual tree-based topic models for translation domain adaptation association for computational linguistics, 2014.
11.
L.Hong and B.D.Davison, Empirical study of topic modeling in twitter, In Workshop on Social Media Analytics (2010).
12.
X.Yan, J.Guo, Y.Lan and X.Cheng, A biterm topic model for short texts, In Proceedings of the 22nd international conference on World Wide Web, ACM, 2013, pp. 1445–1456.
13.
J.Tang, X.Wang, H.Gao, X.Hu and H.Liu, Enriching short text representation in microblog for clustering, Frontiers of Computer Science in China6(1) (2012), 88–101.
14.
G.Tang, Y.Xia, W.Wang, R.Lau and F.Zheng, Clustering tweets using wikipedia concepts, In LREC, 2014, pp. 2262–2267.
15.
P.Bicalho, M.Pita, G.Pedrosa, A.Lacerda and G.L.Pappa, A general framework to expand short text for topic modeling, Information Sciences393 (2017), 66–81.
16.
R.Mehrotra, S.Sanner, W.Buntine and L.Xie, Improving lda topic models for microblogs via tweet pooling and automatic labeling, In SIGIR (2013).
17.
D.Alvarez-Melis and M.Saveski, Topic modeling in twitter: Aggregating tweets by conversations, ICWSM2016 (2016), 519–522.
18.
N.Akhtar and M.M.S.Beg, Improving microblog clustering: Tweet pooling schemes, CSI Journal of Computing (2018), 19–30.
19.
X.Yan, et al., A biterm topic model for short texts, In Proceedings of the 22nd International Conference on World Wide Web ACM, 2013.
20.
W.X.Zhao, et al., Comparing twitter and traditional media using topic models, In European Conference on Information Retrieval, Heidelberg, Berlin, Springer, 2011.
21.
Y.Zuo, J.Zhao and K.Xu, Word network topic model: A simple but general solution for short and imbalanced texts, Knowledge and Information Systems48(2) (2016), 379–398.
22.
J.D.Mcauliffe and D.M.Blei, Supervised topic models, Advances in Neural Information Processing Systems, 2008.
23.
Y.Wang, et al., Hashtag graph based topic model for tweet mining, Data Mining (ICDM)2014 (2014).
24.
F.S.Tsai, Atag-topicmodelforblog mining, Expert Systems with Applications38(5) (2011), 5330–5335.
25.
X.Zhu, D.Blei and J.Lafferty, Taglda: Bringing document structure knowledge into topic models. Technical report, TR-1553, University of Wisconsin, 2006.
26.
S.Li, et al., Tag-weighted dirichlet allocation, Data Mining (ICDM)2013 (2013).
27.
M.Qiu, F.Zhu and J.Jiang, It is not just what we say, but how we say them: Lda-based behavior-topic model, In Proceedings of the 2013 SIAM International Conference on Data Mining Society for Industrial and Applied Mathematics, 2013.
28.
F.Zhao, et al., A personalized hashtag recommendation approach using lda-based topic model in microblog environment, Future Generation Computer Systems65 (2016), 196–206.
29.
Z.Ma, et al., Tag-latent dirichlet allocation: Understanding hashtags and their relationships, In IEEE Computer Society, editor, Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 01, 2013.
30.
M.Rosen-Zvi, et al., The author-topic model for authors and documents, In Auai Press, editor, Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, 2004.
31.
D.M.Blei, A.Y.Ng and M.I.Jordan, Latent dirichlet allocation, Journal of Machine Learning Research3 (2003), 993–1022.
32.
H.Thomas, Probabilistic latent semantic analysis.In Morgan Kaufmann Publishers Inc, editor, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999.
33.
D.M.Blei and J.D.Lafferty, Correlated topic models, In Proceedings of the 18th International Conference on Neural Information Processing Systems, 2005.
34.
D.M.Blei and J.D.Lafferty, Dynamic topic models, In Proceedings of the 23rd International Conference on Machine Learning ACM, 2006.
35.
J.Chang and B.David, Relational topic models for document networks, Artificial Intelligence and Statistics (2009).
36.
T.L.Griffiths, et al., Hierarchical topic models and the nested chinese restaurant process, Advances in Neural Information Processing Systems (2004).
37.
G.Pedrosa, et al., Topic modeling for short texts with cooccurrence frequency-based expansion, Intelligent Systems (BRACIS)2016 (2016), 5.
38.
X.Quan, et al., Short and Sparse Text Topic Modeling via Self-Aggregation, IJCAI (2015).
39.
C.Li, et al., Topic modeling for short texts with auxiliary word embeddings, In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval ACM, 2016.
40.
D.Ramage, et al., Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora, In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1 Association for Computational Linguistics, 2009.
41.
D.Ramage, S.T.Dumais and D.J.Liebling, Characterizing microblogs with topic models, ICWSM10 (2010), 1.
