Deep learning for sentiment analysis
Research and industry are becoming more and more interested in finding automatically the polarised opinion of the general public regarding a specific subject. The advent of social networks has opened the possibility of having access to massive blogs, recommendations, and reviews. The challenge is to...
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| Vydáno v: | Language and linguistics compass Ročník 10; číslo 12; s. 701 - 719 |
|---|---|
| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford
Blackwell Publishing Ltd
01.12.2016
Wiley Subscription Services, Inc |
| Témata: | |
| ISSN: | 1749-818X, 1749-818X |
| On-line přístup: | Získat plný text |
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| Abstract | Research and industry are becoming more and more interested in finding automatically the polarised opinion of the general public regarding a specific subject. The advent of social networks has opened the possibility of having access to massive blogs, recommendations, and reviews. The challenge is to extract the polarity from these data, which is a task of opinion mining or sentiment analysis. The specific difficulties inherent in this task include issues related to subjective interpretation and linguistic phenomena that affect the polarity of words. Recently, deep learning has become a popular method of addressing this task. However, different approaches have been proposed in the literature. This article provides an overview of deep learning for sentiment analysis in order to place these approaches in context. |
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| AbstractList | Research and industry are becoming more and more interested in finding automatically the polarised opinion of the general public regarding a specific subject. The advent of social networks has opened the possibility of having access to massive blogs, recommendations, and reviews. The challenge is to extract the polarity from these data, which is a task of opinion mining or sentiment analysis. The specific difficulties inherent in this task include issues related to subjective interpretation and linguistic phenomena that affect the polarity of words. Recently, deep learning has become a popular method of addressing this task. However, different approaches have been proposed in the literature. This article provides an overview of deep learning for sentiment analysis in order to place these approaches in context. |
| Author | Rojas-Barahona, Lina Maria |
| Author_xml | – sequence: 1 givenname: Lina Maria surname: Rojas-Barahona fullname: Rojas-Barahona, Lina Maria email: lina.rojas@eng.cam.ac.uk, lina.rojas@eng.cam.ac.uk organization: Department of Engineering, University of Cambridge, Cambridge, UK |
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| References_xml | – reference: Bermingham, A., & Smeaton, A. F. (2009). A study of inter-annotator agreement for opinion retrieval. Paper presented at the SIGIR. – reference: Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31, 527-541. – reference: Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine Learning, 30, 271-274. – reference: Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10, 178-185. – reference: Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5, 1-167. – reference: Giachanou, A., & Crestani, F. (2016). Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys, 49(2), 1-41. – reference: Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37, 267-307. – reference: Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press: New York, NY, USA. – reference: Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7, 197-387. – reference: Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12, 2493-2537. – reference: Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning word vectors for sentiment analysis. Association for Computational Linguistics Stroudsburg, PA, USA. – reference: Collobert, R., & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. ACM. – reference: Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9, 1735-1780. – reference: Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on, 5, 157-166. – reference: Martínez-Cámara, E., Teresa Martín-Valdivia, M., Alfonso Urena-López, L., & Rturo Montejo-Ráez, A. (2014). Sentiment analysis in twitter. Natural Language Engineering, 20, 1-28. – year: 2011 – start-page: 815 year: 2011 end-page: 824 – year: 2009 – volume: 7 start-page: 197 year: 2014 end-page: 387 article-title: Deep learning: methods and applications publication-title: Foundations and Trends in Signal Processing – year: 2005 – volume: 37 start-page: 267 year: 2011 end-page: 307 article-title: Lexicon‐based methods for sentiment analysis publication-title: Computational Linguistics – volume: 9 start-page: 1735 year: 1997 end-page: 1780 article-title: Long short‐term memory publication-title: Neural computation – year: 2007 – volume: 30 start-page: 271 year: 1998 end-page: 274 article-title: Glossary of terms publication-title: Machine Learning – volume: 5 start-page: 157 year: 1994 end-page: 166 article-title: Learning long‐term dependencies with gradient descent is difficult publication-title: Neural Networks, IEEE Transactions on – start-page: 377 year: 2014 end-page: 385 – volume: 12 start-page: 2493 year: 2011 end-page: 2537 article-title: Natural language processing (almost) from scratch publication-title: The Journal of Machine Learning Research – year: 2014 – year: 2010 – volume: 31 start-page: 527 year: 2014 end-page: 541 article-title: Sentiment analysis in Facebook and its application to e‐learning publication-title: Computers in Human Behavior – volume: 49 start-page: 1 issue: 2 year: 2016 end-page: 41 article-title: Like it or not: A survey of twitter sentiment analysis methods publication-title: ACM Computing Surveys – year: 2002 – volume: 10 start-page: 178 year: 2010 end-page: 185 article-title: Predicting elections with twitter: What 140 characters reveal about political sentiment publication-title: ICWSM – year: 2008 – year: 2006 – year: 2004 – year: 1995 – volume: 20 start-page: 1 year: 2014 end-page: 28 article-title: Sentiment analysis in twitter publication-title: Natural Language Engineering – start-page: 1 year: 2006 end-page: 10 – year: 2015 – volume: 5 start-page: 1 year: 2012 end-page: 167 article-title: Sentiment analysis and opinion mining publication-title: Synthesis Lectures on Human Language Technologies – year: 2013 – ident: e_1_2_10_10_1 doi: 10.1162/neco.1997.9.8.1735 – ident: e_1_2_10_3_1 doi: 10.1145/1571941.1572127 – ident: e_1_2_10_29_1 doi: 10.1016/j.chb.2013.05.024 – ident: e_1_2_10_39_1 doi: 10.18653/v1/D13-1170 – ident: e_1_2_10_31_1 doi: 10.3115/1118693.1118704 – ident: e_1_2_10_47_1 doi: 10.3115/1220575.1220619 – ident: e_1_2_10_28_1 – ident: e_1_2_10_34_1 doi: 10.3115/v1/S14-2009 – ident: e_1_2_10_7_1 doi: 10.1561/2000000039 – ident: e_1_2_10_21_1 – ident: e_1_2_10_13_1 doi: 10.1145/1935826.1935932 – ident: e_1_2_10_36_1 doi: 10.1609/icwsm.v3i1.13966 – ident: e_1_2_10_38_1 – ident: e_1_2_10_30_1 doi: 10.3115/1219840.1219855 – ident: e_1_2_10_32_1 doi: 10.3115/v1/D14-1162 – ident: e_1_2_10_27_1 – ident: e_1_2_10_41_1 doi: 10.1162/COLI_a_00049 – ident: e_1_2_10_4_1 doi: 10.1093/oso/9780198538493.001.0001 – ident: e_1_2_10_17_1 doi: 10.1023/A:1017181826899 – ident: e_1_2_10_11_1 doi: 10.1145/1014052.1014073 – ident: e_1_2_10_24_1 – ident: e_1_2_10_2_1 doi: 10.1109/72.279181 – ident: e_1_2_10_9_1 doi: 10.1145/2938640 – ident: e_1_2_10_15_1 doi: 10.3115/v1/D14-1181 – ident: e_1_2_10_26_1 – ident: e_1_2_10_20_1 doi: 10.1007/978-3-031-02145-9 – ident: e_1_2_10_43_1 doi: 10.3115/v1/P14-1146 – ident: e_1_2_10_18_1 – ident: e_1_2_10_16_1 doi: 10.3115/1654641.1654642 – ident: e_1_2_10_23_1 doi: 10.1017/S1351324912000332 – volume: 12 start-page: 2493 year: 2011 ident: e_1_2_10_6_1 article-title: Natural language processing (almost) from scratch publication-title: The Journal of Machine Learning Research – ident: e_1_2_10_19_1 doi: 10.18653/v1/D15-1278 – ident: e_1_2_10_25_1 – ident: e_1_2_10_42_1 doi: 10.3115/v1/P15-1150 – ident: e_1_2_10_8_1 doi: 10.3115/v1/E14-1040 – volume-title: Learning word vectors for sentiment analysis year: 2011 ident: e_1_2_10_22_1 – ident: e_1_2_10_33_1 doi: 10.1007/1-4020-4102-0_1 – ident: e_1_2_10_37_1 doi: 10.1609/icwsm.v3i1.14001 – ident: e_1_2_10_5_1 doi: 10.1145/1390156.1390177 – ident: e_1_2_10_35_1 doi: 10.18653/v1/S15-2078 – ident: e_1_2_10_45_1 – ident: e_1_2_10_46_1 doi: 10.3115/1073083.1073153 – ident: e_1_2_10_12_1 doi: 10.3115/v1/D14-1080 – ident: e_1_2_10_14_1 doi: 10.3115/v1/P14-1062 – ident: e_1_2_10_44_1 doi: 10.1609/icwsm.v4i1.14009 – volume-title: Proceedings of the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics year: 2010 ident: e_1_2_10_40_1 |
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