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Evaluation of Data Warehouse Design Methodologies in the Context of Big Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Tag Me a Label with Multi-arm: Active Learning for Telugu Sentiment Analysis

Authors : Sandeep Sricharan Mukku, Subba Reddy Oota, Radhika Mamidi

Published in: Big Data Analytics and Knowledge Discovery

Publisher: Springer International Publishing

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Abstract

Sentiment Analysis is one of the most active research areas in natural language processing and an extensively studied problem in data mining, web mining and text mining for English language. With the proliferation of social media these days, data is widely increasing in regional languages along with English. Telugu is one such regional language with abundant data available in social media, but it’s hard to find a labeled training set as human annotation is time-consuming and cost-ineffective. To address this issue, in this paper the practicality of active learning for Telugu sentiment analysis is investigated. We built a hybrid approach by combining different query selection strategy frameworks to increase more accurate training data instances with limited labeled data. Using a set of classifiers like SVM, XGBoost, and Gradient Boosted Trees (GBT), we achieved promising results with minimal error rate.

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Footnotes
Literature
1.
Settles, B.: Active learning literature survey. Technical report (2010)
2.
Lewis, D.D.: A sequential algorithm for training text classifiers: corrigendum and additional data, pp. 13–19 (1995)
3.
Kolar Rajagopal, A., Subramanian, R., Ricci, E., Vieriu, R.L., Lanz, O., Kalpathi, R., Sebe, N.: Exploring transfer learning approaches for head pose classification from multi-view surveillance images. Int. J. Comput. Vision 109, 146–167 (2014)CrossRef
4.
Huang, S.J., Jin, R., Zhou, Z.H.: Active learning by querying informative and representative examples. In: Proceedings of the 23rd International Conference on Neural Information Processing Systems, pp. 892–900 (2010)
5.
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: EMNLP 2008, pp. 1070–1079. Association for Computational Linguistics (2008)
6.
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 287–294 (1992)
7.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)MATH
8.
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
9.
Ganjisaffar, Y., Caruana, R., Lopes, C.V.: Bagging gradient-boosted trees for high precision, low variance ranking models. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 85–94. ACM (2011)
10.
Motlani, R., Lalwani, H., Shrivastava, M., Sharma, D.M.: Developing part-of-speech tagger for a resource poor language: Sindhi
11.
Gad-Elrab, M.H., Yosef, M.A., Weikum, G.: Named entity disambiguation for resource-poor languages. In: Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR 2015, pp. 29–34 (2015)
12.
Gasser, M.: Expanding the lexicon for a resource-poor language using a morphological analyzer and a web crawler. In: Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, Valletta, Malta, 17–23 May 2010
13.
Sravanthi, M.C., Prathyusha, K., Mamidi, R.: A Dialogue System for Telugu, a Resource-Poor Language, pp. 364–374 (2015)
14.
15.
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 28, 133–168 (1997)CrossRefMATH
16.
Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15, 201–221 (1994)
17.
Chu, W., Zinkevich, M., Li, L., Thomas, A., Tseng, B.: Unbiased online active learning in data streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 195–203 (2011)
18.
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001)MATH
19.
Campbell, C., Cristianini, N., Smola, A., et al.: Query learning with large margin classifiers. In: ICML, pp. 111–118 (2000)
20.
Kremer, J., Steenstrup Pedersen, K., Igel, C.: Active learning with support vector machines. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. 4(4), 313–326 (2014)CrossRef
21.
Fu, Y., Zhu, X., Li, B.: A survey on instance selection for active learning (2012)
22.
Reitmaier, T., Sick, B.: Let us know your decision: pool-based active training of a generative classifier with the selection strategy 4DS. Inf. Sci. 230, 106–131 (2013)CrossRef
23.
24.
Settles, B.: Curious machines: active learning with structured instances. ProQuest (2008)
25.
Zhou, S., Chen, Q., Wang, X.: Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120, 536–546 (2013)CrossRef
26.
Li, S., Ju, S., Zhou, G., Li, X.: Active learning for imbalanced sentiment classification. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, pp. 139–148 (2012)
27.
Boiy, E., Moens, M.F.: A machine learning approach to sentiment analysis in multilingual web texts. Inf. Retrieval 12(5), 526–558 (2009)CrossRef
28.
Mukku, S.S., Choudhary, N., Mamidi, R.: Enhanced sentiment classification of Telugu text using ML techniques. In: Proceedings of 25th International Joint Conference on Artificial Intelligence, p. 29 (2016)
29.
Patra, B.G., Das, D., Das, A., Prasath, R.: Shared task on sentiment analysis in Indian languages (SAIL) tweets - an overview. In: Prasath, R., Vuppala, A.K., Kathirvalavakumar, T. (eds.) MIKE 2015. LNCS, vol. 9468, pp. 650–655. Springer, Cham (2015). doi:10.​1007/​978-3-319-26832-3_​61 CrossRef
30.
Gupta, R., Goyal, P., Diwakar, S.: Transliteration among Indian languages using WX notation. In: KONVENS, pp. 147–150 (2010)
31.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
32.
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:​1301.​3781 (2013)
33.
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)MATH
34.
Krishnamurti, B., Gwynn, J.P.L.: A Grammar of Modern Telugu. Oxford University Press, New York (1985)
35.
Krishnamurthi, B.: Telugu verbal bases: a comparative and descriptive study (1961)
36.
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents
37.
Mahajan, A., Teneketzis, D.: Multi-armed bandit problems. In: Hero, A.O., Castañón, D.A., Cochran, D., Kastella, K. (eds.) Foundations and Applications of Sensor Management, pp. 121–151. Springer, Boston (2008)CrossRef
38.
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240 (2006)
39.
Seewald, A.K.: Meta-learning for stacked classification. Audiology 24(226), 69
Metadata
Title
Tag Me a Label with Multi-arm: Active Learning for Telugu Sentiment Analysis
Authors
Sandeep Sricharan Mukku
Subba Reddy Oota
Radhika Mamidi
Copyright Year
2017
Book
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-319-64282-6

Electronic ISBN: 978-3-319-64283-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-64283-3_26

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