Abstract
The rise of blogs, forums, social networks and review websites in recent years has provided very accessible and convenient platforms for people to express thoughts, views or attitudes about topics of interest. In order to collect and analyse opinionated content on the Internet, various sentiment detection techniques have been developed based on an integration of part-of-speech tagging, negation handling, lexicons and classifiers. A popular unsupervised approach, SO-LSA (Semantic Orientation from Latent Semantic Analysis), uses a term-document matrix to detect the semantic orientation of words according to their similarities to a predefined set of seed terms. This paper proposes a novel and subsymbolic approach in sentiment detection, with a level of accuracy comparable to the baseline, SO-LSA, using a special type of Artificial Neural Networks (ANN), an auto-encoder called Recursive Auto-Associative Memory (RAAM).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chalmers, D.: Syntactic transformations on distributed representations. Connection Science 2(1), 53–62 (1990)
Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Fabianinkatu, pp. 160–167 (July 2008)
Dumais, S., Landauer, T.: A solution to platos problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104, 211–240 (1997)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of Language Resources and Evaluation (LREC), Genoa, Italy (May 2006)
Fellbaum, C., et al.: WordNet: An electronic lexical database. MIT press, Cambridge (1998)
Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2), 110–125 (2006)
Miikkulainen, R., Dyer, M.: Forming global representations with extended backpropagation. In: IEEE International Conference on Neural Networks, pp. 285–292. IEEE (1988)
Moisl, H.: Artificial neural networks and natural language processing. In: Encyclopedia of Library and Information Science, pp. 148–162 (2003)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL), Morristown, NJ, USA, pp. 115–124 (June 2005)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics, Barcelona (2004)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, PA, USA (July 2002)
Pollack, J.: Recursive distributed representations. Artificial Intelligence 46(1-2), 77–105 (1990)
Rusu, D., Dali, L., Fortuna, B., Grobelnik, M., Mladenić, D.: Triplet extraction from sentences. In: 10th International Multiconference Information Society-IS, Ljubljana, Slovenia, pp. 8–12 (July 2007)
Turney, P., Littman, M.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS) 21, 315–346 (2003)
Whitelaw, C., Argamon, S., Garg, N.: Using appraisal taxonomies for sentiment analysis. In: Proceedings of the First Computational Systemic Functional Grammar Conference. University of Sydney, Sydney (2005)
Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Computational Linguistics 30(3), 277–308 (2004)
Wong, C.: Recursive auto-associative memory as connectionist language processing model: training improvements via hybrid neural-genetic schemata. Master’s thesis, City University of Hong Kong (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Danesh, S., Liu, W., French, T., Reynolds, M. (2011). An Investigation of Recursive Auto-associative Memory in Sentiment Detection. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-25853-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25852-7
Online ISBN: 978-3-642-25853-4
eBook Packages: Computer ScienceComputer Science (R0)