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The Second Open Knowledge Extraction Challenge Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

verfasst von : Soufian Jebbara, Philipp Cimiano

Erschienen in: Semantic Web Challenges

Verlag: Springer International Publishing

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Abstract

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained.
We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for Aspect-Based Sentiment Analysis.

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Vorheriges Kapitel A Knowledge-Based Approach for Aspect-Based Opinion Mining
Nächstes Kapitel 6th Open Challenge on Question Answering over Linked Data (QALD-6)
Fußnoten
1
Here, we distinguish between the terminologies of aspect category extraction and aspect term extraction: The set of possible aspect categories is predefined and rather small (e.g. Price, Battery, Accessories, Display, Portability, Camera), while aspect terms can take many shapes (e.g. “sake menu”, “wine selection” or “French Onion soup”).
 
2
Parts of a sentence that refer to an aspect of the product, event, entity, etc.
 
3
For a more convenient notation, we use words and their respective indices interchangeably.
 
4
We use the euclidean vector distance as a distance measure.
 
5
Since this GRU processes the sequence in a reversed direction, the final hidden state is the hidden state for the first word.
 
6
We exclude annotations with aspect = “NULL”.
 
Literatur
1.
Aprosio, A.P., Corcoglioniti, F., Dragoni, M., Rospocher, M.: Supervised opinion frames detection with RAID. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 251–263. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25518-7_​22 CrossRef
2.
Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1515–1521 (2014)
3.
Cho, K., Van Merriënboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1724–1734. Association for Computational Linguistics, October 2014
4.
Chung, J.K.-C., Wu, C.-E., Tsai, R.T.-H.: Polarity detection of online reviews using sentiment concepts: NCU IISR team at ESWC-14 challenge on concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 53–58. Springer, Heidelberg (2014)
5.
Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS Deep Learning Workshop (2014)
6.
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
7.
Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 21–27. Springer, Heidelberg (2014)
8.
Faruqui, M., Dodge, J., Jauhar, S.K., Dyer, C., Hovy, E., Smith, N.A.: Retrofitting word vectors to semantic lexicons. In: Proceedings of Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pp. 1606–1615 (2015)
9.
Fellbaum, C.: WordNet and Wordnets. In: Brown, K. (ed.) Encyclopedia of Language and Linguistics, pp. 665–670. Elsevier, Oxford (2005)
10.
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 168–177. ACM, New York (2004)
11.
Jakob, N., Gurevych, I.: Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045, October 2010
12.
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)
13.
Klinger, R., Cimiano, P.: Bi-directional inter-dependencies of subjective expressions and targets and their value for a joint model. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Short Papers, vol. 2, pp. 848–854, August 2013
14.
Klinger, R., Cimiano, P.: Joint and pipeline probabilistic models for fine-grained sentiment analysis: extracting aspects, subjective phrases and their relations. In: Proceedings of the 13th IEEE International Conference on Data Mining Workshops (ICDM), pp. 937–944, December 2013
15.
Lakkaraju, H., Socher, R., Manning, C.: Aspect specific sentiment analysis using hierarchical deep learning. In: Proceedings of the NIPS Workshop on Deep Learning and Representation Learning (2014)
16.
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. ICML 32, 1188–1196 (2014)
17.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
18.
McAuley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), pp. 785–794. ACM, New York (2015)
19.
McAuley, J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52. ACM (2015)
20.
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (2013)
21.
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)
22.
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543. Association for Computational Linguistics (2014)
23.
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation, Denver, Colorado, pp. 486–495. Association for Computational Linguistics, June 2015
24.
San Vicente, I., Saralegi, X., Agerri, R.: EliXa: a modular and flexible ABSA platform. In: Proceedings of the 9th International Workshop on Semantic Evaluation, Denver, Colorado, pp. 748–752. Association for Computational Linguistics, June 2015
25.
dos Santos, C., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st International Conference on Machine Learning, pp. 1818–1826 (2014)
26.
dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 626–634 (2015)
27.
Schouten, K., Frasincar, F.: The benefit of concept-based features for sentiment analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 223–233. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25518-7_​19 CrossRef
28.
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45(11), 2673–2681 (1997)CrossRef
29.
Sun, Y., Lin, L., Tang, D., Yang, N., Ji, Z., Wang, X.: Modeling mention, context and entity with neural networks for entity disambiguation. In: Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI), pp. 1333–1339. AAAI Press (2015)
30.
Titov, I., Mcdonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), pp. 308–316 (2008)
31.
Tjong Kim Sang, E.F., Veenstra, J.: Representing text chunks. In: Proceedings of European Chapter of the ACL (EACL), Bergen, Norway, pp. 173–179 (1999)
32.
Toh, Z., Wang, W.: DLIREC: aspect term extraction and term polarity classification system. In: Proceedings of the 8th International Workshop on Semantic Evaluation, pp. 235–240 (2014)
33.
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING), pp. 2335–2344 (2014)
Metadaten
Titel
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
verfasst von
Soufian Jebbara
Philipp Cimiano
Copyright-Jahr
2016
Buch
Semantic Web Challenges

Print ISBN: 978-3-319-46564-7

Electronic ISBN: 978-3-319-46565-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46565-4_12

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