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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

Learning Document Representation for Deceptive Opinion Spam Detection

verfasst von : Luyang Li, Wenjing Ren, Bing Qin, Ting Liu

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

Deceptive opinion spam in reviews of products or service is very harmful for customers in decision making. Existing approaches to detect deceptive spam are concern on feature designing. Hand-crafted features can show some linguistic phenomenon, but is time-consuming and can not reveal the connotative semantic meaning of the review. We present a neural network to learn document-level representation. In our model, we not only learn to represent each sentence but also represent the whole document of the review. We apply traditional convolutional neural network to represent the semantic meaning of sentences. We present two variant convolutional neural-network models to learn the document representation. The model taking sentence importance into consideration shows the better performance in deceptive spam detection which enhances the value of F1 by 5 %.

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Vorheriges Kapitel Academic Paper Recommendation Based on Heterogeneous Graph
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Literatur
1.
Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)MATH
2.
Castillo, C., Donato, D., Gionis, A., Murdock, V., Silvestri, F.: Know your neighbors: Web spam detection using the web topology. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 423–430. ACM (2007)
3.
Chirita, P.A., Diederich, J., Nejdl, W.: Mailrank: using ranking for spam detection. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 373–380. ACM (2005)
4.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATH
5.
Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (1999)CrossRef
6.
Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 171–175. Association for Computational Linguistics (2012)
7.
Feng, V.W., Hirst, G.: Detecting deceptive opinions with profile compatibility. In: Proceedings of the 6th International Joint Conference on Natural Language Processing, Nagoya, Japan, pp. 14–18 (2013)
8.
Fetterly, D., Manasse, M., Najork, M.: Detecting phrase-level duplication on the world wide web. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 170–177. ACM (2005)
9.
Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 513–520 (2011)
10.
Gyöngyi, Z., Garcia-Molina, H.: Link spam alliances. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 517–528. VLDB Endowment (2005)
11.
Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.: Combating web spam with trustrank. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 576–587. VLDB Endowment (2004)
12.
Hermann, K.M., Blunsom, P.: The role of syntax in vector space models of compositional semantics. In: ACL, vol. 1, pp. 894–904 (2013)
13.
Huang, E.H., Socher, R., Manning, C.D., Ng, A.Y.: 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, vol. 1, pp. 873–882. Association for Computational Linguistics (2012)
14.
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)
15.
Jindal, N., Liu, B., Lim, E.P.: Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1549–1552. ACM (2010)
16.
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:​1404.​2188 (2014)
17.
18.
19.
Li, J., Jurafsky, D., Hovy, E.: When are tree structures necessary for deep learning of representations? arXiv preprint arXiv:​1503.​00185 (2015)
20.
Li, J., Ott, M., Cardie, C.: Identifying manipulated offerings on review portals. In: EMNLP, pp. 1933–1942 (2013)
21.
Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam
22.
Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 939–948. ACM (2010)
23.
Metaxas, P.T., DeStefano, J.: Web spam, propaganda and trust. In: AIRWeb, pp. 70–78 (2005)
24.
Meyer, D.: Fake reviews prompt belkin apology. CNet News (2009)
25.
Miller, C.: Company settles case of reviews it faked. New York Times (2009)
26.
Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)
27.
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., Ghosh, R.: Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 632–640. ACM (2013)
28.
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, pp. 191–200. ACM (2012)
29.
Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting group review spam. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 93–94. ACM (2011)
30.
Ntoulas, A., Najork, M., Manasse, M., Fetterly, D.: Detecting spam web pages through content analysis. In: Proceedings of the 15th International Conference on World Wide Web, pp. 83–92. ACM (2006)
31.
Ott, M.: Computational linguistic models of deceptive opinion spam (2013)
32.
Ott, M., Cardie, C., Hancock, J.: Estimating the prevalence of deception in online review communities. In: Proceedings of the 21st International Conference on World Wide Web, pp. 201–210. ACM (2012)
33.
Ott, M., Cardie, C., Hancock, J.T.: Negative deceptive opinion spam. In: HLT-NAACL, pp. 497–501 (2013)
34.
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 309–319. Association for Computational Linguistics (2011)
35.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014) vol. 12, pp. 1532–1543 (2014)
36.
Socher, R., Bauer, J., Manning, C.D., Ng, A.Y.: Parsing with compositional vector grammars. In: Proceedings of the ACL Conference. Citeseer (2013)
37.
Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1201–1211. Association for Computational Linguistics (2012)
38.
Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 129–136 (2011)
39.
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), vol. 1631, p. 1642. Citeseer (2013)
40.
Streitfeld, D.: For 2 a star, an online retailer gets 5 star product reviews. New York Times, 26 January 2012
41.
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1555–1565 (2014)
42.
Topping, A.: Historian orlando figes agrees to pay damages for fake reviews. The Guardian, 16 July 2010
43.
Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010)
44.
Wang, G., Xie, S., Liu, B., Yu, P.S.: Review graph based online store review spammer detection. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 1242–1247. IEEE (2011)
45.
Wu, B., Davison, B.D.: Identifying link farm spam pages. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, pp. 820–829. ACM (2005)
46.
Yessenalina, A., Cardie, C.: Compositional matrix-space models for sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 172–182. Association for Computational Linguistics (2011)
47.
Yoo, K.H., Gretzel, U.: Comparison of deceptive and truthful travel reviews. In: Höpken, W., Gretzel, U., Law, R. (eds.) Information and Communication Technologies in Tourism, pp. 37–47. Springer, Heidelberg (2009)
Metadaten
Titel
Learning Document Representation for Deceptive Opinion Spam Detection
verfasst von
Luyang Li
Wenjing Ren
Bing Qin
Ting Liu
Copyright-Jahr
2015
Buch
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-319-25815-7

Electronic ISBN: 978-3-319-25816-4

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-25816-4_32