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

  • D. VivekEmail author
  • P. Balasubramanie


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.


Unstructured Data n-grams Phrases MDD Depression Tree bank Perplexity Polarity 



  1. 1.
    Akay A, Dragomir A (2016) Monitoring and Mining of Online Fora 20, 977–986Google Scholar
  2. 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 19:210–218CrossRefGoogle Scholar
  3. 3.
    Bandhakavi A, Wiratunga N, Massie S, Padmanabhan D (2017) Lexicon Generation for Emotion Detection from Text. IEEE Intell Syst 32:102–108CrossRefGoogle Scholar
  4. 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. 1–4Google Scholar
  5. 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 19:1347–1355CrossRefGoogle Scholar
  6. 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 19:1246–1252CrossRefGoogle Scholar
  7. 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 47:273–284CrossRefGoogle Scholar
  8. 8.
    Lovato P, Cristani M, Bicego M (2016) Soft Ngram representation and modeling for protein remote homology detection. IEEE/ACM Trans Comput Biol Bioinform 5963:1–1Google Scholar
  9. 9.
    Luo Z, Liu L, Yin J, Li IY, Wu Z (2017) Deep Learning of Graphs with Ngram Convolutional Neural Networks. 4347, 1–14Google Scholar
  10. 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. 156–161Google Scholar
  11. 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 7:59–73CrossRefGoogle Scholar
  12. 12.
    Shafran I, Mohri M (2005) A comparison of classifiers for detecting emotion from speech. ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc. IGoogle Scholar
  13. 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 21:407–415CrossRefGoogle Scholar
  14. 14.
    Stratou G, Morency L (2014) MultiSense - Context-aware Nonverbal Behavior Analysis Framework : A Psychological Distress Use Case. J LateX Cl Files 13:190–203Google Scholar
  15. 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 3536:1–11Google Scholar
  16. 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 20:987–995CrossRefGoogle Scholar
  17. 17.
    Thomas B, Dhanya KA, Vinod P (2014) Synthesized feature space for multiclass emotion classification. 1st Int. Conf. Networks Soft Comput. ICNSC 2014 - Proc. 188–192Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information & Communication EngineeringAnna UniversityChennaiIndia
  2. 2.Department of Computer Science and EngineeringKongu Engineering CollegeErodeIndia

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