Skip to main content
SpringerLink
Log in

A Distributed Ensemble of Deep Convolutional Neural Networks with Random Forest for Big Data Sentiment Analysis

  • Conference paper
  • First Online:
Mobile, Secure, and Programmable Networking (MSPN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11557))

Abstract

Big data has become an important issue for a large number of research areas. With the advent of social networks, users can express their feelings about the products they bought or the services they used every day. Also, they can share their ideas and interests, discuss current issues. Therefore, Big Data sentiment analysis has become important in decision-making processes. In this paper, we propose a novel distributed ensemble of deep convolutional neural networks with random forest for sentiment analysis, which is tailored to handle large-scale data and improve classification accuracy. Experimental results on two real-world data sets confirm the claim.

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 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Chen, X., Ishwaran, H.: Random forests for genomic data analysis. Genomics 99(6), 323–329 (2012)

    Article  Google Scholar 

  2. Wyner, A.J., Olson, M., Bleich, J., Mease, D.: Explaining the success of adaboost and random forests as interpolating classifiers. J. Mach. Learn. Res. 18(1), 1558–1590 (2017)

    MathSciNet  MATH  Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  4. Genuer, R., Poggi, J.M., Tuleau-Malot, C., Villa-Vialaneix, N.: Random forests for big data. Big Data Res. 9, 28–46 (2017)

    Article  Google Scholar 

  5. Chen, J., Li, K., Tang, Z., Bilal, K., Yu, S., Weng, C., Li, K.: A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 28, 919–933 (2017)

    Article  Google Scholar 

  6. Hammou, B.A., Lahcen, A.A., Mouline, S.: APRA: an approximate parallel recommendation algorithm for big data. Knowl.-Based Syst. 157, 10–19 (2018)

    Article  Google Scholar 

  7. Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-37882-2

    Book  MATH  Google Scholar 

  8. 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)

    Google Scholar 

  9. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  10. Xie, Y., Le, L., Zhou, Y., Raghavan, V.V.: Deep Learning for Natural Language Processing. Handbook of Statistics (2018)

    Google Scholar 

  11. Kim, H.K., Kim, H., Cho, S.: Bag-of-concepts: comprehending document representation through clustering words in distributed representation. Neurocomputing 266, 336–352 (2017)

    Article  Google Scholar 

  12. Rezaeinia, S.M., Rahmani, R., Ghodsi, A., Veisi, H.: Sentiment analysis based on improved pre-trained word embeddings. Expert Syst. Appl. 117, 139–147 (2019)

    Article  Google Scholar 

  13. Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866 (2014)

  14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  15. Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, no. 1, p. 1237, July 2011

    Google Scholar 

  16. 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, pp. 115–124. Association for Computational Linguistics, June 2005

    Google Scholar 

  17. Sentiment140. https://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip. Accessed 31 Jan 2018

  18. Chen, T., Xu, R., He, Y., Wang, X.: Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst. Appl. 72, 221–230 (2017)

    Article  Google Scholar 

  19. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

  20. Word2Vec. https://code.google.com/archive/p/word2vec/. Accessed 31 Jan 2018

  21. Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 151–161. Association for Computational Linguistics, July 2011

    Google Scholar 

  22. Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)

    Google Scholar 

  23. Irsoy, O., Cardie, C.: Deep recursive neural networks for compositionality in language. In: Advances in Neural Information Processing Systems, pp. 2096–2104 (2014)

    Google Scholar 

  24. Ren, Y., Wang, R., Ji, D.: A topic-enhanced word embedding for Twitter sentiment classification. Inf. Sci. 369, 188–198 (2016)

    Article  Google Scholar 

  25. Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820 (2015)

  26. Jianqiang, Z., Xiaolin, G.: Comparison research on text pre-processing methods on Twitter sentiment analysis. IEEE Access 5, 2870–2879 (2017)

    Article  Google Scholar 

  27. Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics, July 2012

    Google Scholar 

  28. Cambria, E., Rajagopal, D., Olsher, D., Das, D.: Big social data analysis. Big Data Comput. 13, 401–414 (2013)

    Article  Google Scholar 

  29. Pandarachalil, R., Sendhilkumar, S., Mahalakshmi, G.S.: Twitter sentiment analysis for large-scale data: an unsupervised approach. Cogn. Comput. 7(2), 254–262 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LRIT, Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco

    Badr Ait Hammou, Ayoub Ait Lahcen & Salma Mouline

  2. LGS, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra, Morocco

    Ayoub Ait Lahcen

Authors
  1. Badr Ait Hammou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayoub Ait Lahcen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salma Mouline
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Badr Ait Hammou .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Évry, France

    Éric Renault

  2. Conservatoire National des Arts et Métiers, Paris, France

    Selma Boumerdassi

  3. Hassan II Mohammedia University, Mohammedia, Morocco

    Cherkaoui Leghris

  4. Conservatoire National des Arts et Métiers, Paris, France

    Samia Bouzefrane

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ait Hammou, B., Ait Lahcen, A., Mouline, S. (2019). A Distributed Ensemble of Deep Convolutional Neural Networks with Random Forest for Big Data Sentiment Analysis. In: Renault, É., Boumerdassi, S., Leghris, C., Bouzefrane, S. (eds) Mobile, Secure, and Programmable Networking. MSPN 2019. Lecture Notes in Computer Science(), vol 11557. Springer, Cham. https://doi.org/10.1007/978-3-030-22885-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22885-9_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22884-2

  • Online ISBN: 978-3-030-22885-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation