Skip to main content
SpringerLink
Log in

An Expressive phrases identification supported with feature prediction consuming unstructured data collection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

It’s evident that, the rate of unstructured data is increasing from different sources of social media. In these days those data are being used by many researchers for their producing research results. The sentiment analysis is one of the key research perspective predicted by analysing unstructured data, but these analyses are majorly used for business intelligence. Hence, the huge unstructured data related to medical intelligence is not used properly. In this paper, the domain is introduced in sentiment analysis in the field of medical intelligence. Herewith the Major Depression Disorder (MDD) phrases are predicted by positive and negative polarity calculation. The phrases are framed by generating the UN-gram classification methodology, which is continuously splits the sentences to identify the exact emotional phrases. The experimental results are statistically proven by the probability distribution of n-gram classifier, which is compared with the sentiment tree bank generated with the value of perplexity and polarity distribution.

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

Access this article

Log in via an institution

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
Fig. 9

Similar content being viewed by others

References

  1. Akay A, Dragomir A (2016) Monitoring and Mining of Online Fora 20, 977–986

  2. Akay A, Dragomir A, Erlandsson B-E (2015) Network-based modeling and intelligent data mining of social media for improving care. IEEE J Biomed Heal inform 19:210–218

    Article  Google Scholar 

  3. Bandhakavi A, Wiratunga N, Massie S, Padmanabhan D (2017) Lexicon Generation for Emotion Detection from Text. IEEE Intell Syst 32:102–108

    Article  Google Scholar 

  4. Dumoulin J (2012) Smoothing of ngram language models of human chats. 6th Int. Conf. Soft Comput. Intell. Syst. 13th Int. Symp. Adv. Intell. Syst. SCIS/ISIS 2012. 1–4

  5. Kim J, Nakamura T, Kikuchi H, Yoshiuchi K, Sasaki T, Yamamoto Y (2015) Covariation of Depressive Mood and Spontaneous Physical Activity in Major Depressive Disorder: Toward Continuous Monitoring of Depressive Mood. IEEE J Biomed Heal Inform 19:1347–1355

    Article  Google Scholar 

  6. Larsen ME, Boonstra TW, Batterham PJ, O’Dea B, Paris C, Christensen H (2015) We Feel: Mapping Emotion on Twitter. IEEE J Biomed Health Inform 19:1246–1252

    Article  Google Scholar 

  7. Likforman-Sulem L, Esposito A, Faundez-Zanuy M, Clemencon S, Cordasco G (2017) EMOTHAW: A Novel Database for Emotional State Recognition from Handwriting and Drawing. IEEE Trans Human-Mach Syst 47:273–284

    Article  Google Scholar 

  8. Lovato P, Cristani M, Bicego M (2016) Soft Ngram representation and modeling for protein remote homology detection. IEEE/ACM Trans Comput Biol Bioinform 5963:1–1

    Google Scholar 

  9. Luo Z, Liu L, Yin J, Li IY, Wu Z (2017) Deep Learning of Graphs with Ngram Convolutional Neural Networks. 4347, 1–14

  10. Schall M, Schambach MP, Franz MO (2016) Increasing Robustness of Handwriting Recognition Using Character N-Gram Decoding on Large Lexica. Proc. - 12th IAPR Int. Work. Doc. Anal. Syst. DAS 2016. 156–161

  11. Scherer S, Lucas GM, Gratch J, Rizzo A, Morency LP (2016) Self-Reported Symptoms of Depression and PTSD Are Associated with Reduced Vowel Space in Screening Interviews. IEEE Trans Affect Comput 7:59–73

    Article  Google Scholar 

  12. Shafran I, Mohri M (2005) A comparison of classifiers for detecting emotion from speech. ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc. I

  13. Sim SY, Joo KM, Kim HB, Jang S, Kim B, Hong S, Kim S, Park KS (2017) Estimation of Circadian Body Temperature Rhythm Based on Heart Rate in Healthy. Ambulatory Subjects IEEE J Biomed Heal Inform 21:407–415

    Article  Google Scholar 

  14. Stratou G, Morency L (2014) MultiSense - Context-aware Nonverbal Behavior Analysis Framework : A Psychological Distress Use Case. J LateX Cl Files 13:190–203

    Google Scholar 

  15. Subhani AR, Mumtaz W, Saad MNBM, Kamel N, Malik AS (2017) Machine Learning Framework for the Detection of Mental Stress at Multiple Levels. IEEE Access 3536:1–11

    Google Scholar 

  16. Tai CH, Tan ZH, Chang YS (2016) Systematical Approach for Detecting the Intention and Intensity of Feelings on Social Network. IEEE J Biomed Heal Inform 20:987–995

    Article  Google Scholar 

  17. Thomas B, Dhanya KA, Vinod P (2014) Synthesized feature space for multiclass emotion classification. 1st Int. Conf. Networks Soft Comput. ICNSC 2014 - Proc. 188–192

Download references

Author information

Authors and Affiliations

  1. Department of Information & Communication Engineering, Anna University, Chennai, Tamil Nadu, India

    D. Vivek

  2. Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India

    P. Balasubramanie

Authors
  1. D. Vivek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Balasubramanie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Vivek.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vivek, D., Balasubramanie, P. An Expressive phrases identification supported with feature prediction consuming unstructured data collection. Multimed Tools Appl 79, 3791–3806 (2020). https://doi.org/10.1007/s11042-018-7059-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7059-1

Keywords

Search

Navigation