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Erschienen in:
A Comparison Among Significance Tests and Other Feature Building Methods for Sentiment Analysis: A First Study Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Comment Relevance Classification in Facebook

verfasst von : Chaya Liebeskind, Shmuel Liebeskind, Yaakov HaCohen-Kerner

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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Abstract

Social posts and their comments are rich and interesting social data. In this study, we aim to classify comments as relevant or irrelevant to the content of their posts. Since the comments in social media are usually short, their bag-of-words (BoW) representations are highly sparse. We investigate four semantic vector representations for the relevance classification task. We investigate different types of large unlabeled data for learning the distributional representations. We also empirically demonstrate that expanding the input of the task to include the post text does not improve the classification performance over using only the comment text. We show that representing the comment in the post space is a cheap and good representation for comment relevance classification.

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Vorheriges Kapitel Supervised Domain Adaptation via Label Alignment for Opinion Expression Extraction
Nächstes Kapitel Detecting Sockpuppets in Deceptive Opinion Spam
Fußnoten
3
In all the reported experiments, statistical significant was measured according to the paired t-test at the 0.05 level.
 
Literatur
1.
Chang, Y., Wang, X., Mei, Q., Liu, Y.: Towards Twitter context summarization with user influence models. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 527–536. ACM (2013)
2.
Raut, V.B., Londhe, D.: Survey on opinion mining and summarization of user reviews on web. Int. J. Comput. Sci. Inf. Technol. 5, 1026–1030 (2014)
3.
4.
Woodward, E.V., Yan, S.: Socially derived translation profiles to enhance translation quality of social content using a machine translation. Google Patents (2016)
5.
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014)CrossRef
6.
Baldwin, T., De Marneffe, M.C., Han, B., Kim, Y.-B., Ritter, A., Xu, W.: Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition. In: Proceedings of the Workshop on Noisy User-Generated Text (WNUT 2015), Beijing, China (2015)
7.
Nakov, P., Rosenthal, S., Kiritchenko, S., Mohammad, S.M., Kozareva, Z., Ritter, A., Stoyanov, V., Zhu, X.: Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts. Lang. Resour. Eval. 50, 35–65 (2016)CrossRef
8.
Liebeskind, C., Nahon, K., Hacohen-Kerner, Y., Manor, Y.: Comparing sentiment analysis models to classify attitudes of political comments on Facebook. Polibits Res. J. Comput. Sci. Comput. Eng. Appl. (2016)
9.
Manning, C.D., Raghavan, P., Schütze, H., Others.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
10.
Zhang, Y., Jin, R., Zhou, Z.-H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. Cybern. 1, 43–52 (2010)CrossRef
11.
Wang, M., Cao, D., Li, L., Li, S., Ji, R.: Microblog sentiment analysis based on cross-media bag-of-words model. In: Proceedings of International Conference on Internet Multimedia Computing and Service, p. 76. ACM (2014)
12.
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391 (1990)CrossRef
13.
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25, 259–284 (1998)CrossRef
14.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
15.
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245–250. ACM (2001)
16.
Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th International Conference on World Wide Web, pp. 377–386. ACM (2006)
17.
Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using Wikipedia. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 787–788. ACM (2007)
18.
Hu, X., Sun, N., Zhang, C., Chua, T.-S.: Exploiting internal and external semantics for the clustering of short texts using world knowledge. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 919–928. ACM (2009)
19.
Song, G., Ye, Y., Du, X., Huang, X., Bie, S.: Short text classification: a survey. J. Multimed. 9, 635–643 (2014)CrossRef
20.
Zhang, H., Zhong, G.: Improving short text classification by learning vector representations of both words and hidden topics. Knowl.-Based Syst. 102, 76–86 (2016)CrossRef
21.
Ma, C., Xu, W., Li, P., Yan, Y.: Distributional representations of words for short text classification. In: Proceedings of NAACL-HLT, pp. 33–38 (2015)
22.
Zelikovitz, S., Hirsh, H.: Transductive LSI for short text classification problems. In: FLAIRS Conference, pp. 556–561 (2004)
23.
Zelikovitz, S., Marquez, F.: Transductive learning for short-text classification problems using latent semantic indexing. Int. J. Pattern Recognit Artif Intell. 19, 143–163 (2005)CrossRef
24.
25.
Baker, L.D., McCallum, A.K.: Distributional clustering of words for text classification. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 96–103. ACM (1998)
26.
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)
27.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
28.
Minka, T., Lafferty, J.: Expectation-propagation for the generative aspect model. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 352–359. Morgan Kaufmann Publishers Inc. (2002)
29.
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101, 5228–5235 (2004)CrossRef
30.
Phan, X.-H., Nguyen, L.-M., Horiguchi, S.: 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, pp. 91–100. ACM (2008)
31.
Chen, M., Jin, X., Shen, D.: Short text classification improved by learning multi-granularity topics. In: IJCAI, pp. 1776–1781. Citeseer (2011)
32.
Wang, B., Huang, Y., Yang, W., Li, X.: Short text classification based on strong feature thesaurus. J. Zhejiang Univ. Sci. C. 13, 649–659 (2012)CrossRef
33.
Johnson, W.B., Lindenstrauss, J.: Extensions of Lipschitz mappings into a Hilbert space. Contemp. Math. 26, 1 (1984)MathSciNetCrossRef
34.
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)
35.
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for Twitter sentiment classification. In: ACL, vol. 1, pp. 1555–1565 (2014)
36.
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP, pp. 1422–1432 (2015)
37.
Wang, X., Jiang, W., Luo, Z.: Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In: Proceedings of the 26th International Conference on Computational Linguistics, pp. 2428–2437 (2016)
38.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of NAACL-HLT, pp. 1480–1489 (2016)
39.
Dumais, S.T., Letsche, T.A., Littman, M.L., Landauer, T.K.: Automatic cross-language retrieval using latent semantic indexing. In: AAAI Spring Symposium on Cross-Language Text and Speech Retrieval, p. 21 (1997)
40.
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics, 159–174 (1977)
41.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRef
42.
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2016)
43.
Faqeeh, M., Abdulla, N., Al-Ayyoub, M., Jararweh, Y., Quwaider, M.: Cross-lingual short-text document classification for Facebook comments. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 573–578. IEEE (2014)
44.
Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., Stoyanov, V.: SemEval-2016 task 4: sentiment analysis in Twitter. In: Proceedings of SemEval, 1–18 (2016)
45.
Petz, G., Karpowicz, M., Fürschuß, H., Auinger, A., Stříteský, V., Holzinger, A.: Opinion mining on the web 2.0 – characteristics of user generated content and their impacts. In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 35–46. Springer, Heidelberg (2013). https://​doi.​org/​10.​1007/​978-3-642-39146-0_​4CrossRef
46.
Dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, pp. 69–78 (2014)
47.
Metadaten
Titel
Comment Relevance Classification in Facebook
verfasst von
Chaya Liebeskind
Shmuel Liebeskind
Yaakov HaCohen-Kerner
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-77115-1

Electronic ISBN: 978-3-319-77116-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77116-8_18