{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:02:36Z","timestamp":1760241756329,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T00:00:00Z","timestamp":1533254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities of Central South University","award":["No.2018zzts587"],"award-info":[{"award-number":["No.2018zzts587"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex.<\/jats:p>","DOI":"10.3390\/info9080198","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T11:03:26Z","timestamp":1533294206000},"page":"198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Aspect Term Extraction Based on MFE-CRF"],"prefix":"10.3390","volume":"9","author":[{"given":"Yanmin","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Central South University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongye","family":"He","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Central South University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Central South University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,3]]},"reference":[{"key":"ref_1","first-page":"25","article-title":"Current State of Text Sentiment Analysis from Opinion to Emotion Mining","volume":"50","author":"Yadollahi","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_2","first-page":"28","article-title":"Like It or Not: A Survey of Twitter Sentiment Analysis Methods","volume":"49","author":"Giachanou","year":"2016","journal-title":"ACM Comput. Surv."},{"key":"ref_3","first-page":"1","article-title":"Sentiment Analysis and Opinion Mining","volume":"30","author":"Liu","year":"2012","journal-title":"Synth. Lect. Hum. Lang. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1177\/0165551510388123","article-title":"Aspect-based sentiment analysis of movie reviews on discussion boards","volume":"36","author":"Thet","year":"2010","journal-title":"J. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wen, H., and Zhao, J. (2017, January 15\u201317). Aspect term extraction of E-commerce comments based on model ensemble. Proceedings of the 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China.","DOI":"10.1109\/ICCWAMTIP.2017.8301421"},{"key":"ref_6","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed Representations of Words and Phrases and their Compositionality. Proceedings of the Advances in Neural Information Processing Systems, Stateline, NV, USA."},{"key":"ref_7","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. Comput. Sci."},{"key":"ref_8","unstructured":"(2018, July 25). One-Hot. Available online: https:\/\/en.wikipedia.org\/wiki\/One-hot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Choi, Y., Cardie, C., Riloff, E., and Patwardhan, S. (2005, January 6\u20138). Identifying sources of opinions with conditional random fields and extraction patterns. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, BC, Canada.","DOI":"10.3115\/1220575.1220620"},{"key":"ref_10","unstructured":"Jakob, N., and Gurevych, I. (2011, January 27\u201331). Extracting Opinion Targets in a Single- and Cross-Domain Setting with Conditional Random Fields. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK."},{"key":"ref_11","unstructured":"Miao, Q., Li, Q., and Zeng, D. (September, January 31). Mining Fine Grained Opinions by Using Probabilistic Models and Domain Knowledge. Proceedings of the IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Toronto, ON, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Toh, Z., and Wang, W. (2014, January 23\u201324). DLIREC: Aspect Term Extraction and Term Polarity Classification System. Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, Ireland.","DOI":"10.3115\/v1\/S14-2038"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Parkhe, V., and Biswas, B. (2014, January 26\u201327). Aspect Based Sentiment Analysis of Movie Reviews: Finding the Polarity Directing Aspects. Proceedings of the International Conference on Soft Computing and Machine Intelligence, New Delhi, India.","DOI":"10.1109\/ISCMI.2014.16"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guha, S., Joshi, A., and Varma, V. (2015, January 4\u20135). SIEL: Aspect Based Sentiment Analysis in Reviews. Proceedings of the 9th International Workshop on Semantic Evaluation, Denver, CO, USA.","DOI":"10.18653\/v1\/S15-2129"},{"key":"ref_15","first-page":"61","article-title":"TASS 2014. The Challenge of Aspect-based Sentiment Analysis","volume":"54","author":"Morera","year":"2015","journal-title":"Proces. Del Leng. Nat."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Mohammad, A.S., Al-Ayyoub, M., Zhao, Y., Qin, B., and De Clercq, O. (2014, January 23\u201324). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of the International Workshop on Semantic Evaluation, Dublin, Ireland.","DOI":"10.3115\/v1\/S14-2004"},{"key":"ref_17","first-page":"41","article-title":"Dependency Tree-Based Rules for Concept-Level Aspect-Based Sentiment Analysis","volume":"475","author":"Poria","year":"2014","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_18","first-page":"22","article-title":"Aspect-based Sentiment Analysis on a Large-Scale Data: Topic Models are the Preferred Solution","volume":"8","author":"Khalid","year":"2015","journal-title":"Bahria Univ. J. Inf. Commun. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Poria, S., Chaturvedi, I., Cambria, E., and Bisio, F. (2016, January 24\u201329). Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727784"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Schouten, K., Baas, F., Bus, O., Osinga, A., van de Ven, N., van Loenhout, S., Vrolijk, L., and Frasincar, F. (2016, January 7\u201310). Aspect-Based Sentiment Analysis Using Lexico-Semantic Patterns. Proceedings of the Web Information Systems Engineering\u2014WISE 2016, Shanghai, China.","DOI":"10.1007\/978-3-319-48743-4_3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.knosys.2018.01.019","article-title":"A Hybrid Unsupervised Method for Aspect Term and Opinion Target Extraction","volume":"148","author":"Wu","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s11280-015-0381-x","article-title":"Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier","volume":"20","author":"Manek","year":"2017","journal-title":"World Wide Web"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MIS.2017.57","article-title":"Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams","volume":"32","author":"Weichselbraun","year":"2017","journal-title":"IEEE Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Schusterb\u00f6ckler, B., and Bateman, A. (2007). An Introduction to Hidden Markov Models. Curr. Protoc. Bioinform., 18.","DOI":"10.1002\/0471250953.bia03as18"},{"key":"ref_25","unstructured":"Lafferty, J., McCallum, A., and Pereira, F.C. (July, January 28). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, P., Joty, S., and Meng, H. (2015, January 17\u201321). Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1168"},{"key":"ref_27","first-page":"100","article-title":"Algorithm AS 136: A K-Means Clustering Algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc."},{"key":"ref_28","unstructured":"Arthur, D., and Vassilvitskii, S. (2007, January 7\u20139). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Acm-Siam Symposium on Discrete Algorithms, New Orleans, LA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chernyshevich, M. (2014, January 23\u201324). IHS R&D Belarus: Cross-domain extraction of product features using CRF. Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, Ireland.","DOI":"10.3115\/v1\/S14-2051"},{"key":"ref_30","unstructured":"Vicente, I.S., Saralegi, X., and Agerri, R. (2015, January 4\u20135). EliXa: A modular and flexible ABSA platform. Proceedings of the 9th International Workshop on Semantic Evaluation, Denver, CO, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Toh, Z., and Su, J. (2016, January 16\u201317). NLANGP at semeval-2016 task 5: Improving aspect based sentiment analysis using neural network features. Proceedings of the 10th International Workshop on Semantic Evaluation, San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1045"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, W., Pan, S.J., Dahlmeier, D., and Xiao, X. (2017, January 4\u20139). Coupled multi-layer attentions for co-extraction of aspect and opinion terms. Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10974"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/8\/198\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:16:26Z","timestamp":1760195786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/8\/198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,3]]},"references-count":32,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["info9080198"],"URL":"https:\/\/doi.org\/10.3390\/info9080198","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2018,8,3]]}}}