Skip to main content
SpringerLink
Log in

Extreme Learning Machine for Multi-class Sentiment Classification of Tweets

  • Conference paper
  • First Online:
Proceedings of ELM-2015 Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 6))

Abstract

The increasing popularity of social media in recent years has created new opportunities to study and evaluate public opinions and sentiments for use in marketing and social behavioural studies. However, binary classification into positive and negative sentiments may not reveal too much information about a product or service. This research paper explores the multi-class sentiment classification using machine learning methods. Three machine learning methods are investigated in this paper to examine their respective performance in multi-class sentiment classification of tweets. Experimental results show that Extreme Learning Machine (ELM) achieves better performance than other machine learning methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  2. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Mining Text Data, pp. 415–463. Springer (2012)

    Google Scholar 

  3. Wang, Z., Tong, V.J.C., Chan, D.: Issues of social data analytics with a new method for sentiment analysis of social media data. In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom 2014), pp. 899–904. IEEE (2014)

    Google Scholar 

  4. Wang, Z., Tong, V.J.C., Chin, H.C.: Enhancing machine-learning methods for sentiment classification of web data. In: Information Retrieval Technology, pp. 394–405. Springer (2014)

    Google Scholar 

  5. Huang, G.-B.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)

    Article  Google Scholar 

  6. Huang, G.-B., Bai, Z., Kasun, L.L.C., Vong, C.M.: Local receptive fields based extreme learning machine. IEEE Comput. Intell. Mag. 10(2), 18–29 (2015)

    Article  Google Scholar 

  7. Liang, N.-Y., Saratchandran, P., Huang, G.-B., Sundararajan, N.: Classification of mental tasks from eeg signals using extreme learning machine. Int. J. Neural Syst. 16(01), 29–38 (2006)

    Article  Google Scholar 

  8. Handoko, S.D., Keong, K.C., Soon, O.Y., Zhang, G.L., Brusic, V.: Extreme learning machine for predicting hla-peptide binding. In: Advances in Neural Networks-ISNN 2006, pp. 716–721. Springer (2006)

    Google Scholar 

  9. Yeu, C.-W., Lim, M.-H., Huang, G.-B., Agarwal, A., Ong, Y.-S.: A new machine learning paradigm for terrain reconstruction. IEEE Geosci. Remote Sens. Lett. 3(3), 382–386 (2006)

    Article  Google Scholar 

  10. Kim, J., Shin, H., Lee, Y., Lee, M.: Algorithm for classifying arrhythmia using extreme learning machine and principal component analysis. In: Engineering in Medicine and Biology Society: EMBS 2007. 29th Annual International Conference of the IEEE, pp. 3257–3260. IEEE (2007)

    Google Scholar 

  11. Wang, G., Zhao, Y., Wang, D.: A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing 72(1), 262–268 (2008)

    Article  Google Scholar 

  12. Chaovalit, P., Zhou, L.: Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences. HICSS’05, pp. 112c–112c. IEEE (2005)

    Google Scholar 

  13. Galitsky, B., McKenna, E.W.: Sentiment extraction from consumer reviews for providing product recommendations, May 12, 2008, US Patent App. 12/119,465

    Google Scholar 

  14. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)

    Google Scholar 

  15. Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic based twitter sentiment for stock prediction. In: ACL (2), pp. 24–29 (2013)

    Google Scholar 

  16. Chi, L., Zhuang, X., Song, D.: Investor sentiment in the chinese stock market: an empirical analysis. Appl. Econ. Lett. 19(4), 345–348 (2012)

    Article  Google Scholar 

  17. Dalton, L.A., Dougherty, E.R.: Optimal classifiers with minimum expected error within a bayesian framework part ii: properties and performance analysis. Pattern Recognit. 46(5), 1288–1300 (2013)

    Article  MATH  Google Scholar 

  18. Muralidharan, V., Sugumaran, V.: A comparative study of naïve bayes classifier and bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl. Soft Comput. 12(8), 2023–2029 (2012)

    Article  Google Scholar 

  19. Pappas, E., Kotsiantis, S.: Integrating global and local application of discriminative multinomial bayesian classifier for text classification. In: Intelligent Informatics, pp. 49–55. Springer (2013)

    Google Scholar 

  20. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Techn. (TIST) 2(3), 27 (2011)

    Google Scholar 

  21. Byvatov, E., Fechner, U., Sadowski, J., Schneider, G.: Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43(6), 1882–1889 (2003)

    Article  Google Scholar 

  22. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks. Proceedings, vol. 2, pp. 985–990. IEEE (2004)

    Google Scholar 

  23. Huang, G.-B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014)

    Article  Google Scholar 

  24. Rao, C.R., Mitra, S.K.: Generalized Inverse of Matrices and Its Applications, vol. 7. Wiley, New York (1971)

    Google Scholar 

  25. Maršík, J., Bojar, O.: Trtok: a fast and trainable tokenizer for natural languages. Prague Bull. Math. Linguist. 98, 75–85 (2012)

    Google Scholar 

  26. Willett, P.: The porter stemming algorithm: then and now. Program 40(3), 219–223 (2006)

    Article  MathSciNet  Google Scholar 

  27. Bergstra, J., Yamins, D., Cox, D.D.: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms (2013)

    Google Scholar 

  28. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the A*STAR Joint Council Office Development Programme “Social Technologies+ Programme”.

Author information

Authors and Affiliations

  1. Social and Cognitive Computing (SCC) Department, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore, 138632, Singapore

    Zhaoxia Wang

  2. Department of Space, Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram, 695547, India

    Yogesh Parth

Authors
  1. Zhaoxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yogesh Parth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoxia Wang .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, Z., Parth, Y. (2016). Extreme Learning Machine for Multi-class Sentiment Classification of Tweets. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28397-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28396-8

  • Online ISBN: 978-3-319-28397-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation