Sage Journals: Discover world-class research

Abstract

This article introduces a different method for text representation in order to perform clustering over different articles which, arguably, has no subjective information with similar topic-sentiment use of language. Using the joint sentiment/topic model, the text is vectorized in a low dimensional space. These vectors were then used as distance measurement for clustering texts. While comparing this unusual method with a traditional bag ofwords representation an improvement in the performance of the algorithms was observed. The authors think this method of representation might have implications for future studies of the computational interpretation of texts.

Keywords

Text representation Joint Sentiment Topic Modeling text clustering

Get full access to this article

View all access options for this article.

References

Arora and

Ravindran , Latent dirichlet allocation based multi-document summarization, In Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data, 2008, pp. 91–97. ACM.

Baroni ,

Dinu and

Kruszewski , Don’t count, predict! a systematic comparison of contextcounting vs. context-predicting semantic vectors, In Proceedings of the 52nd Annual Meeting ofthe Association for Computational Linguistics (Volume 1: Long Papers), volume 1,2014, pages 238–247.

Bengio ,

Ducharme ,

Vincent and

Jauvin , A neural probabilistic language model, Journal of Machine Learning Research3(Feb) (2003), 1137–1155.

D.M.

Blei ,

A.Y.

Ng and

M.I.

Jordan , Latent dirichlet allocation, Journal of Machine Learning Research3(Jan) (2003), 993–1022.

Brychcín and

Konopík , Semantic spaces for improving language modeling, Computer Speech & Language28(1) (2014), 192–209. ISSN 0885-2308. 10.1016/j.csl.2013.05.001.

B.C.

Maicas , Análisis de la evolución de los géneros periodísticos informativos: El caso del periódico el país, 2015.

Collobert ,

Weston ,

Bottou ,

Karlen ,

Kavukcuoglu and

Kuksa , Natural language processing (almost) from scratch, Journal of Machine Learning Research12(Aug) (2011), 2493–2537.

D.R.

Cutting ,

D.R.

Karger ,

J.O.

Pedersen and

J.W.

Tukey , Scatter/gather: A cluster-based approach to browsing large document collections, In Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’92, New York, NY, USA, 1992, pp. 318–329. ACM. ISBN 0-89791523-2. 10.1145/133160.133214

I.D.

Rangel ,

Sidorov and

S.S.

Guerra , Creación y evaluación de un diccionario marcado con emociones y ponderado para el español, Onomazein1(29) (2014).

10.

Gómez-Adorno ,

Sidorov ,

Pinto ,

Vilariño and

Gelbukh , Automatic authorship detection using textual patterns extracted from integrated syntactic graphs, Sensors16(9) (2016), 1374.

11.

Gomis , Teoría de los géneros periodísticos, volume 1. Editorial UOC, 2008.

12.

Z.S.

Harris , Distributional structure, Distributional Structure (1954), 3–22. cited By 63.

13.

Hofmann , Probabilistic latent semantic analysis, In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 1999, pp. 289–296.

14.

E.H.

Huang ,

Socher ,

C.D.

Manning and

A.Y.

Ng , Improving word representations via global context and multiple word prototypes, In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, Association for Computational Linguistics, 2012, pp. 873–882.

15.

Kaufman and

P.J.

Rousseeuw , Finding groups in data: An introduction to cluster analysis, volume 344. John Wiley & Sons, 2009.

16.

Lin and

He , Joint sentiment/topic model for sentiment analysis, In Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009, pp. 375–384. ACM.

17.

Lin ,

He ,

Everson and

Ruger , Weakly supervised joint sentiment-topic detection from text, IEEE Transactions on Knowledge and Data Engineering24(6) (2012), 1134–1145.

18.

Mikolov ,

Chen ,

Corrado and

Dean , Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.378I (2013).

19.

Mnih and

Hinton , Three new graphical models for statistical language modelling, In Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 641–648. ACM.

20.

Mnih and

G.E.

Hinton , A scalable hierarchical distributed language model, In Advances in Neural Information Processing Systems, 2009, pp. 1081–1088.

21.

Momtazi , Unsupervised latent dirichlet allocation for supervised question classification, Information Processing & Management54(3) (2018), 380–393.

22.

Rosenberg and

Hirschberg , V-measure: A conditional entropy-based external cluster evaluation measure, In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 2007.

23.

P.J.

Rousseeuw , Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics20 (1987), 53–65. ISSN 0377-042710.1016/0377-0427(87)90125-7.

24.

Rubinstein ,

Levi ,

Schwartz and

Rappoport , How well do distributional models capture different types of semantic knowledge? In Proceedings ofthe 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 20I5, Beijing, China, Volume 2: Short Papers, 2015, pp. 726–730.

25.

Sahlgren , The distributional hypothesis, Italian Journal of Disability Studies20 (2008), 33–53.

26.

M.M.

Shafiei and

E.E.

Milios , Latent dirichlet coclustering, In Data Mining, 2006 ICDM’06. Sixth International Conference on, IEEE, 2006, pp. 542–551.

27.

Sidorov ,

Miranda-Jiménez ,

Viveros-Jiménez ,

Gelbukh ,

Castro-Sánchez ,

Velásquez ,

Díaz-Rangel ,

Suárez-Guerra ,

Treviño and

Gordon , Empirical study of machine learning based approach for opinion mining in tweets, In Mexican International Conference on Artificial Intelligence, 2012, pp. 1–14. Springer.

28.

Stamatatos ,

Tschuggnall ,

Verhoeven ,

Daele-mans ,

Specht ,

Stein and

Potthast , Clustering by Authorship Within and Across Documents, In Working Notes Papers of the CLEF 2016 Evaluation Labs, volume 1609 of CEUR Workshop Proceedings CLEF and CEUR-WS.org, 2016.

29.

Steinbach ,

Karypis and

Kumar , A comparison of document clustering techniques, In KDD Workshop on Text Mining, 2000.

30.

Turian ,

Ratinov and

Bengio , Word representations: A simple and general method for semi-supervised learning, In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2010, pp. 384–394.

31.

P.D.

Turney , Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews, In Proceedings ofthe 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 2002, pp. 417–424.