Advertisement

Fitting a Neural Network Classification Model in MATLAB and R for Tweeter Data set

  • Syed Muzamil BashaEmail author
  • Dharmendra Singh Rajput
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

Nowadays, the interest among the research community in sentiment analysis (SA) has grown exponentially. Our paper aims to find the prediction error occurred when we perform SA on tweets. The data set considered for the demonstration has 1129 tweets, and output parameters having predictor identifiers. Artificial neural networks (ANNs) are designed with ten hidden layers and one output layer. Additionally, trained the designed system with the help of MATLAB software to find the prediction error and also, derived sentiments using ggplot2 package in R.

Keywords

SA ANN ggplot2 R 

References

  1. 1.
    Bhardwaj, A., Tiwari, A., Bhardwaj, H., Bhardwaj, A.: A genetically optimized neural network model for multi-class classification. Expert Syst. Appl. 60, 211–221 (2016)CrossRefGoogle Scholar
  2. 2.
    Nogueira, K., Penatti, O.A., dos Santos, J.A.: Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recogn. 61, 539–556 (2017)CrossRefGoogle Scholar
  3. 3.
    Bai, X., Shi, B., Zhang, C., Cai, X., Qi, L.: Text/non-text image classification in the wild with convolutional neural networks. Pattern Recognit. (2016)Google Scholar
  4. 4.
    Tan, Y., Tang, P., Zhou, Y., Luo, W., Kang, Y., Li, G.: Photograph aesthetical evaluation and classification with deep convolutional neural networks. Neurocomputing, 534–547 (2016)Google Scholar
  5. 5.
    Mohammed, M.F., Lim, C.P.: Improving the fuzzy min-max neural network with a k-nearest hyperbox expansion rule for pattern classification. Appl. Soft Comput., 12–19 (2016)Google Scholar
  6. 6.
    Qawaqneh, Z., Mallouh, A.A., Barkana, B.D.: Deep neural network framework and transformed fMFCCsg for speaker’s age and gender classification. Knowl. Based Syst. 115, 5–14 (2017)CrossRefGoogle Scholar
  7. 7.
    Khokhar, S., Zin, A.A.M., Memon, A.P., Mokhtar, A.S.: A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement 95, 246–259 (2017)CrossRefGoogle Scholar
  8. 8.
    Tjolleng, A., Jung, K., Hong, W., Lee, W., Lee, B., You, H., Son, J., Park, S.: Classification of a driver’s cognitive workload levels using artificial neural network on fECGg signals. Appl. Ergon. 59(Part A), 326–332 (2017)Google Scholar
  9. 9.
    Gutirrez-Gnecchi, J.A., Morfin-Magaa, R., Lorias-Espinoza, D., del Carmen Tellez-Anguiano, A., Reyes-Archundia, E., Mndez-Patio, A., Castaeda-Miranda, R.: DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomed. Signal Process. Control 32, 44–56 (2017)CrossRefGoogle Scholar
  10. 10.
    Jagtap, J., Kokare, M.: Human age classification using facial skin aging features and artificial neural network. Cogn. Syst. Res. 40, 116–128 (2016)CrossRefGoogle Scholar
  11. 11.
    Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., Zang, Y., Tian, J.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn. 61, 663–673 (2017)CrossRefGoogle Scholar
  12. 12.
    Denisov, I.A.: Luciferase-based bioassay for rapid pollutants detection and classification by means of multilayer artificial neural networks. Sens. Actuators B: Chem. 242, 653–657 (2017)CrossRefGoogle Scholar
  13. 13.
    Tang, Z., Li, C., Sun, S.: Single-trial fEEGg classification of motor imagery using deep convolutional neural networks. Optik Int. J. Light Electron Opt. 130, 11–18 (2017)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Xie, W., Li, H.: Hyperspectral image reconstruction by deep convolutional neural network for classification. Pattern Recogn. 63, 371–383 (2017)CrossRefGoogle Scholar
  15. 15.
    Li, K., Wu, X., Meng, H.: Intonation classification for fL2g english speech using multi-distribution deep neural networks. Comput. Speech Lang. 43, 18–33 (2017)CrossRefGoogle Scholar
  16. 16.
    Satapathy, S.K., Dehuri, S., Jagadev, A.K.: fEEGg signal classification using fPSOg trained fRBFg neural network for epilepsy identification. Inf. Med. Unlocked 6, 1–11 (2017)Google Scholar
  17. 17.
    Binetti, G., Del Coco, L., Schena, F.P.: Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, fNIRg and merceological data. Food Chem. 219, 131–138 (2017)CrossRefGoogle Scholar
  18. 18.
    Miki, Y., Muramatsu, C., Hayashi, T., Zhou, X., Hara, T., Katsumata, A., Fujita, H.: Classification of teeth in cone-beam fCTg using deep convolutional neural network. Comput. Biol. Med. 80, 24–29 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Vellore Institute of Technology UniversityVelloreIndia

Personalised recommendations