Skip to main content

Advertisement

Springer Nature Link
Log in

Ensemble application of ELM and GPU for real-time multimodal sentiment analysis

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

The enormous number of videos posted everyday on multimedia websites such as Facebook and YouTube makes the Internet an infinite source of information. Collecting and processing such information, however, is a very challenging task as it involves dealing with a huge amount of information that is changing at a very high speed. To this end, we leverage on the processing speed of extreme learning machine and graphics processing unit to overcome the limitations of standard learning algorithms and central processing unit (CPU) and, hence, perform real-time multimodal sentiment analysis, i.e., harvesting sentiments from web videos by taking into account audio, visual and textual modalities as sources of the information. For the sentiment classification, we leveraged on sentic memes, i.e., basic units of sentiment whose combination can potentially describe the full range of emotional experiences that are rooted in any of us, including different degrees of polarity. We used both feature and decision level fusion methods to fuse the information extracted from the different modalities. Using the sentiment annotated dataset generated from YouTube video reviews, our proposed multimodal system is shown to achieve an accuracy of 78%. In term of processing speed, our method shows improvements of several orders of magnitude for feature extraction compared to CPU-based counterparts.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. https://nvlabs.github.io/moderngpu/scan.html.

References

  1. Alm CO, Roth D, Sproat R (2005) Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, pp 579–586

  2. Busso C, Narayanan SS (2007) Interrelation between speech and facial gestures in emotional utterances: a single subject study. IEEE Trans Audio Speech Lang Process 15(8):2331–2347

    Article  Google Scholar 

  3. Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107

    Article  Google Scholar 

  4. Cambria E, Hussain A (2015) Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Springer, Cham

    Book  Google Scholar 

  5. Cambria E, Poria S, Bajpai R, Schuller B (2016) SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: the 26th international conference on computational linguistics (COLING), pp 2666–2677

  6. Chaumartin F-R (2007) Upar7: a knowledge-based system for headline sentiment tagging. In: Proceedings of the 4th international workshop on semantic evaluations. Association for Computational Linguistics, pp 422–425

  7. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  8. Ekman P, Keltner D (1970) Universal facial expressions of emotion. Calif Ment Health Res Dig 8(4):151–158

    Google Scholar 

  9. El Ayadi M, Kamel MS, Karray F (2011) Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recognit 44(3):572–587

    Article  MATH  Google Scholar 

  10. Huang G-B, Cambria E, Toh K-A, Widrow B, Xu Z (2015) New trends of learning in computational intelligence. IEEE Comput Intell Mag 10(2):16–17

    Article  Google Scholar 

  11. Johnstone T (1996) Emotional speech elicited using computer games. In: Proceedings, 4th international conference on spoken language, 1996. ICSLP 96. vol 3. IEEE, pp 1985–1988

  12. Li J, Lu Y, Pu B, Xie Y, Qin J, Pang W-M, Heng P-A (2009) Accelerating active shape model using GPU for facial extraction in video. In: IEEE international conference on intelligent computing and intelligent systems, 2009. ICIS 2009, vol 4. IEEE, pp 522–526

  13. Li X, Mao W, Jiang W, Yao Y (2016) Extreme learning machine via free sparse transfer representation optimization. Memet Comput 8(2):85–95

    Article  Google Scholar 

  14. Majumder N, Poria S, Gelbukh A, Cambria E (2017) Deep learning based document modeling for personality detection from text. IEEE Intell Syst 32(2):42–49

    Article  Google Scholar 

  15. Michálek J, Vaněk J (2014) An open-source GPU-accelerated feature extraction tool. In: 2014 12th international conference on signal processing (ICSP). IEEE, pp 450–454

  16. Morency L-P, Mihalcea R, Doshi P (2011) Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th international conference on multimodal interfaces. ACM, pp 169–176

  17. Murray IR, Arnott JL (1993) Toward the simulation of emotion in synthetic speech: a review of the literature on human vocal emotion. J Acoust Soc Am 93(2):1097–1108

    Article  Google Scholar 

  18. Oneto L, Bisio F, Cambria E, Anguita D (2016) Statistical learning theory and ELM for big social data analysis. IEEE Comput Intell Mag 11(3):45–55

    Article  Google Scholar 

  19. Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl-Based Syst 108:42–49

    Article  Google Scholar 

  20. Poria S, Chaturvedi I, Cambria E, Hussain A (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: ICDM. Barcelona, pp 439–448

  21. Poria S, Cambria E, Bajpai R, Hussain A (2017) A review of affective computing: from unimodal analysis to multimodal fusion. Inf Fus 37:98–125

    Article  Google Scholar 

  22. Sattar A, Seguier R (2010) Hmoam: hybrid multi-objective genetic optimization for facial analysis by appearance model. Memet Comput 2(1):25–46

    Article  Google Scholar 

  23. Song M, You M, Li N, Chen C (2008) A robust multimodal approach for emotion recognition. Neurocomputing 71(10):1913–1920

    Article  Google Scholar 

  24. Tran H-N, Cambria E, Hussain A (2016) Towards GPU-based common-sense reasoning: using fast subgraph matching. Cognit Comput 8(6):1074–1086

    Article  Google Scholar 

  25. Turney PD (2002) Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 417–424

  26. Várkonyi-Kóczy AR (2010) New advances in digital image processing. Memet Comput 2(4):283–304

    Article  Google Scholar 

  27. Wiebe J, Riloff E (2005) Creating subjective and objective sentence classifiers from unannotated texts. In: International conference on intelligent text processing and computational linguistics. Springer, pp 486–497

  28. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, pp 347–354

  29. Xiao C, Dong Z, Xu Y, Meng K, Zhou X, Zhang X (2016) Rational and self-adaptive evolutionary extreme learning machine for electricity price forecast. Memet Comput 8(3):223–233

    Article  Google Scholar 

  30. Yang C, Lin KH-Y, Chen H-H (2007) Building emotion lexicon from weblog corpora. In: Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions. Association for Computational Linguistics, pp 133–136

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Ha-Nguyen Tran & Erik Cambria

Authors
  1. Ha-Nguyen Tran
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Erik Cambria
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ha-Nguyen Tran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tran, HN., Cambria, E. Ensemble application of ELM and GPU for real-time multimodal sentiment analysis. Memetic Comp. 10, 3–13 (2018). https://doi.org/10.1007/s12293-017-0228-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-017-0228-3

Keywords