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Abstract

Topic Modeling encompasses a set of techniques for text clustering and tag recommendation with significant advantages such as unsupervised learning. Based on Latent Dirichlet Allocation (LDA) topic modeling, every single word is related to a set of topics with different weight. The weights are further estimated in order to determine the semantic relation between the words and the rest of the documents. Apparently the chief drawback of topic modeling techniques, specifically LDA, lies on their incapability in clustering short texts in which semantic relation between words is neglected. This issue is deemed more severe when analyzing social networks such as Twitter wherein short texts are the case. It is assumed that semantic relation between a document and the target short text helps obtain efficient clustering of short texts via topic modeling. Hence, the current paper proposes a method of topic modeling named Semantic Knowledge LDA based on semantic relations between the words in tweets from Twitter social network based on the co-occurrence of words. Additionally, we propose a method of hashtag recommender system based on topic vector (TV) text similarity, named TV based Hashtag Recommender System (TVHRS). Accordingly, we applied our word co-occurrence LDA (SKLDAC) method together with WordNet lexical database to cluster the short texts from Twitter. The clustered topics are later used as the repository for the proposed hashtag recommender system. The proposed system of both SKLDA and TVHRS were applied to a set of 12,309,911 real tweets for testing purposes. When comparing the components of the proposed system to the existing methods, we recorded higher performance in terms of precision, recall and F-Score of 0.551, 0.682 and 0.526, respectively.

Keywords

Topic modeling short text hashtag recommendation LDA semantic LDA Twitter

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References

Atefeh

and Khreich

, A survey of techniques for event detection in twitter, Computational Intelligence 31(1) (2015), 132–164.

Blei

D.M.

A.Y.

and Jordan

M.I.

, Latent dirichlet allocation, J. Mach. Learn. Res. 3 (2003), 993–1022.

Feldman

and Sanger

, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press, 2007.

Godin

Slavkovikj

De Neve

Schrauwen

and Van de Walle

, Using topic models for twitter hashtag recommendation, in: Proceedings of the 22Nd International Conference on World Wide Web, WWW ’13 Companion, New York, NY, USA, ACM, 2013, pp. 593–596.

Gong

Zhang

and Huang

, Hashtag recommendation for multimodal microblog posts, Neurocomputing 272(Supplement C) (2018), 170–177.

Jamali

and Abolhassani

, Different aspects of social network analysis, in: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI ’06, Washington, DC, USA, 2006, pp. 66–72. IEEE Computer Society.

Jin

Liu

N.N.

Zhao

and Yang

, Transferring topical knowledge from auxiliary long texts for short text clustering, in: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, New York, NY, USA, ACM, 2011, pp. 775–784.

Kolb

, Disco: A multilingual database of distributionally similar words, in: In Proceedings of KONVENS, 2008.

Kolb

, Experiments on the difference between semantic similarity and relatedness, in: In Proceedings of the 17th Nordic Conference on Computational Linguistics – NODALIDA ’09, 2009.

10.

Lee

D.H.

and Brusilovsky

, Improving personalized recommendations using community membership information, Information Processing & Management 53(5) (2017), 1201–1214.

11.

and Xu

, Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content, Knowledge-Based Systems 106(Supplement C) (2016), 196–205.

12.

Wang

Deng

Wang

and Chang

K.C.-C.

, Towards social user profiling: Unified and discriminative influence model for inferring home locations, in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, New York, NY, USA, ACM, 2012, pp. 1023–1031.

13.

Rao

Xie

Liu

Wong

T.-L.

and Wang

F.L.

, Social emotion classification based on noise-aware training, Data & Knowledge Engineering, 2017.

14.

Liu

Jiang

and Zhang

, Hashtag recommendation with topical attention-based lstm, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, Coling, 2016, pp. 3019–3029.

15.

Cai

Ajiferuke

and Wolfram

, Vocabulary size and its effect on topic representation, Information Processing & Management 53(3) (2017), 653–665.

16.

Mehrotra

Sanner

Buntine

and Xie

, Improving lda topic models for microblogs via tweet pooling and automatic labeling, in: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’13, New York, NY, USA, ACM, 2013, pp. 889–892.

17.

Miller

G.A.

, Wordnet: A lexical database for english, Commun. ACM 38(11) (1995), 39–41.

18.

Otsuka

Wallace

S.A.

and Chiu

, A hashtag recommendation system for twitter data streams, Computational Social Networks 3(1) (2016).

19.

Patel

and Mistry

, A review: Text classification on social media data, IOSR Journal of Computer Engineering 17(1) (2015), 80–84.

20.

Perry

J.W.

Kent

and Berry

M.M.

, Machine literature searching x. machine language; factors underlying its design and development, Journal of the Association for Information Science and Technology 6(4) (1955), 242–254.

21.

Phan

X.-H.

Nguyen

L.-M.

and Horiguchi

, Learning to classify short and sparse text & web with hidden topics from large-scale data collections, in: Proceedings of the 17th International Conference on World Wide Web, WWW ’08, New York, NY, USA, ACM, 2008, pp. 91–100.

22.

Qiang

Chen

Wang

and Wu

, Topic modeling over short texts by incorporating word embeddings, in: Advances in Knowledge Discovery and Data Mining, 21st Pacific-Asia Conference, PAKDD 2017, Cham, Springer International Publishing, 2017, pp. 363–374.

23.

Saif

Fernandez

and Alani

, Contextual semantics for sentiment analysis of twitter, Information Processing & Management 52(1) (2016), 5–19. Emotion and Sentiment in Social and Expressive Media.

24.

She

and Chen

, Tomoha: Topic model-based hashtag recommendation on twitter, in: Proceedings of the 23rd International Conference on World Wide Web, WWW ’14 Companion, New York, NY, USA, ACM, 2014, pp. 371–372.

25.

Steinskog

A.O.

Therkelsen

J.F.

and Gambäck

, Twitter topic modeling by tweet aggregation, in: Proceedings of the 21st Nordic Conference of Computational Linguistics, Linkoping University Electronic Press, 2017, pp. 77–86.

26.

Tajbakhsh

M.S.

and Bagherzadeh

, Microblogging hash tag recommendation system based on semantic tf-idf: Twitter use case, in: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2016, pp. 252–257.

27.

Vathi

Siolas

and Stafylopatis

, Mining and categorizing interesting topics in twitter communities, Journal of Intelligent and Fuzzy Systems 32 (2017), 1265–1275.

28.

Vavliakis

K.N.

Symeonidis

A.L.

and Mitkas

P.A.

, Event identification in web social media through named entity recognition and topic modeling, Data & Knowledge Engineering 88(Supplement C) (2013), 1–24.

29.

Zhao

Zhu

Jin

and Yang

L.T.

, A personalized hashtag recommendation approach using lda-based topic model in microblog environment, Future Generation Computer Systems 65(Supplement C) (2016), 196–206. Special Issue on Big Data in the Cloud.

30.

Zhao

Zhu

Jin

and Yang

L.T.

, A personalized hashtag recommendation approach using lda-based topic model in microblog environment, Future Generation Computer Systems 65 (2016), 196–206. Special Issue on Big Data in the Cloud.

31.

Zhao

W.X.

Jiang

Weng

Lim

E.-P.

Yan

and Li

, Comparing twitter and traditional media using topic models, in: Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18–21, 2011. Proceedings, Berlin, Heidelberg, Springer, 2011, pp. 338–349.

32.

Zirn

and Stuckenschmidt

, Multidimensional topic analysis in political texts, Data & Knowledge Engineering 90(Supplement C) (2014), 38–53. Special Issue on Natural Language Processing and Information Systems (NLDB 2012).

Semantic knowledge LDA with topic vector for recommending hashtags: Twitter use case

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