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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| Published in: | Language and linguistics compass Vol. 10; no. 12; pp. 701 - 719 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Blackwell Publishing Ltd
01.12.2016
Wiley Subscription Services, Inc |
| Subjects: | |
| ISSN: | 1749-818X, 1749-818X |
| Online Access: | Get full 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. 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| Title | Deep learning for sentiment analysis |
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