Abstract
Sentiment analysis, also named opinion mining, is an important task in e-commerce. Recent years, many researchers have been focused on fine-grained sentiment analysis. Aspect level opinion mining detects the detailed sentiments about features of products. However, current aspect identification methods mainly focus on extracting explicit appeared aspects. The task of implicit aspect identification is still a big challenge in sentiment analysis. In this paper, we propose a novel implicit aspect identification approach based on non-negative matrix factorization. The approach first clusters product aspects by combining the co-occurrence information with intra-relations of aspect and opinion words, which can enhance the performance of aspect clustering substantially. In the next step, the approach collects context information of aspects, and represents review sentences by word vectors. Finally, a classifier is constructed to identify and predict the target implicit aspects. We also prove the convergence of our approach. Experimental results demonstrate that our approach outperforms baseline methods in most cases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
The code and data are publicly available at https://github.com/nan0606/implicit-aspect-identification.
References
Chen J, Sun L, Peng YL, Huang Y (2015) Context weight considered for implicit feature extracting. In: IEEE International Conference on Data Science and Advanced Analytics. IEEE, pp 1–5
Chen L, Martineau J, Cheng D, Sheth A (2016) Clustering for simultaneous extraction of aspects and features from reviews. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 789–799
Dai T, Zhu L, Cai X, Pan S, Yuan S (2017) Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network. J Ambient Intell Hum Comput 9:1–19
Ding CH, Li T, Peng W, Park H (2006) Orthogonal nonnegative matrix t-factorizations for clustering. Knowledge discovery and data mining
Fei G, Liu B, Hsu M, Castellanos M, Ghosh R (2012) A dictionary-based approach to identifying aspects implied by adjectives for opinion mining. In: 24th international conference on computational linguistics
Hai Z, Chang K, Kim JJ (2011) Implicit feature identification via co-occurrence association rule mining. In: International Conference on Computational Linguistics and Intelligent Text Processing, vol 101. Springer-Verlag, pp 393–404
Hai Z, Chang K, Cong G (2012) One seed to find them all: mining opinion features via association. In: ACM International Conference on Information and Knowledge Management. ACM, pp 255–264
Hai Z, Chang K, Cong G, Yang CC (2015) An association-based unified framework for mining features and opinion words. ACM Trans Intell Syst Technol 6(2):1–21
Hajar EH, Mohammed B (2016) Hybrid approach to extract adjectives for implicit aspect identification in opinion mining. In: 11th international conference on intelligent systems: theories and applications (SITA). IEEE
Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, August. DBLP, pp 168–177
Jakob N, Gurevych I (2010) Extracting opinion targets in a single and cross-domain setting with conditional random fields. Empirical methods in natural language processing
Jin W, Ho HH (2009) A novel lexicalized HMM-based learning framework for web opinion mining. In: International conference on machine learning
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791
Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. Neural information processing systems
Li T, Zhang Y, Sindhwani V (2009) A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge. In: International joint conference on natural language processing
Li Z, Wu X, Peng H (2010) Nonnegative matrix factorization on orthogonal subspace. Elsevier Science Inc., New York
Liang J, Zhou X, Guo L, Bai S (2015) Feature selection for sentiment classification using matrix factorization. In: International world wide web conferences
Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the Web. In: International World Wide Web Conference, pp 342–351
Liu L, Lv Z, Wang H (2013) Extract product features in Chinese web for opinion mining. J Softw 8(3):627–632
Liu Q, Gao Z, Liu B, Zhang Y (2015) Automated rule selection for aspect extraction in opinion mining. In: International Conference on Artificial Intelligence. AAAI Press, pp 1291–1297
Makadia N, Chaudhuri A, Vohra S (2016) Aspect-based opinion summarization for disparate features. Int J Adv Res Innovative Ideas Educ 2(3):8
Moghaddam S, Ester M (2011) ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. ACM, pp 665–674
Panchendrarajan R, Ahamed N, Murugaiah B, Sivakumar P, Ranathunga S, Pemasiri A (2016) Implicit aspect detection in restaurant reviews using cooccurence of words. The Workshop on Computational Approaches To Subjectivity, pp 128–136
Popescu AM, Etzioni O (2005) Extracting product features and opinions from reviews. Natural language processing and text mining. Springer, London
Poria S, Cambria E, Ku L, Gui C, Gelbukh AF (2014) A rule-based approach to aspect extraction from product reviews. In: International conference on computational linguistics
Prasojo RE, Kacimi M, Nutt W (2015) Entity and aspect extraction for organizing news comments, pp 233–242
Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. In: International Jont Conference on Artifical Intelligence, vol 38. Morgan Kaufmann Publishers Inc, pp 1199–1204
Rana TA, Cheah YN (2015) Hybrid rule-based approach for aspect extraction and categorization from customer reviews. International Conference on It in Asia. IEEE, pp 1–5
Santu SKK, Sondhi P, Zhai CX (2016) Generative feature language models for mining implicit features from customer reviews. pp 929–938
Schouten K, Frasincar F (2014) Finding implicit features in consumer reviews for sentiment analysis. In: International Conference on Web Engineering, vol 8541. Springer, Cham, pp 130–144
Shu L, Xu H, Liu B (2017) Lifelong learning crf for supervised aspect extraction
Su Q, Xu X, Guo H, Guo Z, Wu X, Zhang X et al (2008) Hidden sentiment association in chinese web opinion mining. International world wide web conferences, pp 959–968
Sun L, Li S, Li JY, Lv JT (2015) A novel context-based implicit feature extracting method. International Conference on Data Science and Advanced Analytics. IEEE, pp 420–424
Sun L, Chen J, Li J, Peng Y (2016) Joint topic-opinion model for implicit feature extracting. International Conference on Intelligent Systems and Knowledge Engineering. IEEE, pp 208–213
Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network, pp 214–224
Wagstaff K, Cardie C, Rogers S (2001) Constrained K-means clustering with background knowledge. Eighteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., pp 577–584
Wang W, Xu H, Wan W (2013) Implicit feature identification via hybrid association rule mining. Pergamon Press Inc., Tarrytown
Xu X, Cheng X, Tan S, Yue L, Shen H (2013) Aspect-level opinion mining of online customer reviews. China Commun 10(3):25–41
Xu H, Zhang F, Wang W (2015) Implicit feature identification in chinese reviews using explicit topic mining model. Knowl Based Syst 76:166–175
Yan Z, Xing M, Zhang D, Ma B (2015) Exprs: an extended pagerank method for product feature extraction from online consumer reviews. Inf Manag 52(7):850–858
Yang L, Cai X, Pan S, Dai H, Mu D, Yang L et al (2017) Multi-document summarization based on sentence cluster using non-negative matrix facorization. J Intell Fuzzy Syst 33(1):1–13
Yu J, Zha ZJ, Wang M, Wang K, Chua TS (2012) Domain-assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews. Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27–31 July 2011, John Mcintyre Conference Centre, Edinburgh, Uk, A Meeting of Sigdat, A Special Interest Group of the ACL. DBLP, pp 140–150
Zeng L, Li F (2013) A classification-based approach for implicit feature identification. Springer, Berlin
Zhang Y, Zhu W (2013) Extracting implicit features in online customer reviews for opinion mining. In: International Conference on World Wide Web, vol 30. ACM, pp 103–104
Zhang L, Liu B, Lim SH, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. In: International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, pp 1462–1470
Zhang W, Xu H, Wan W (2012) Weakness finder: find product weakness from chinese reviews by using aspects based sentiment analysis. Expert Syst Appl 39(11):10283–10291
Acknowledgements
The work described in this paper was partially support by National Natural Science Foundation of China (Project No. 61373046) and Natural Science Basic Research Plan in Shaanxi Province of China (Project No. S2015YFJM2129).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xu, Q., Zhu, L., Dai, T. et al. Non-negative matrix factorization for implicit aspect identification. J Ambient Intell Human Comput 11, 2683–2699 (2020). https://doi.org/10.1007/s12652-019-01328-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01328-9