Performance Analysis of Hyperparameters on a Sentiment Analysis Model

  • I. A. Kandhro Department of Computer Science, Sindh Madressatul Islam University, Pakistan
  • S. Z. Jumani Department of Computer Science, Sindh Madressatul Islam University, Pakistan
  • F. Ali Department of Software Engineering, Sir Syed University of Engineering and Technology , Karachi, Pakistan
  • Z. U. Shaikh Department of Software Engineering, Sindh Madressatul Islam University, Pakistan
  • M. A. Arain Department of Software Engineering, Sindh Madressatul Islam University, Pakistan
  • A. A. Shaikh Department of Software Engineering, Sindh Madressatul Islam University, Pakistan
Keywords: LSTM, student feedback, sentiment analysis, performance analysis

Abstract

This paper focuses on the performance analysis of hyperparameters of the Sentiment Analysis (SA) model of a course evaluation dataset. The performance was analyzed regarding hyperparameters such as activation, optimization, and regularization. In this paper, the activation functions used were adam, adagrad, nadam, adamax, and hard_sigmoid, the optimization functions were softmax, softplus, sigmoid, and relu, and the dropout values were 0.1, 0.2, 0.3, and 0.4. The results indicate that parameters adam and softmax with dropout value 2.0 are effective when compared to other combinations of the SA model. The experimental results reveal that the proposed model outperforms the state-of-the-art deep learning classifiers.

Downloads

Download data is not yet available.

References

S. M. Kim and R. A. Calvo, “Sentiment analysis in student experiences of learning,” presented at the 3rd International Conference on Educational Data Mining, Pittsburgh, PA, USA, Jun. 2010, pp. 111–120.

C. K. Leong, Y. H. Lee, and W. K. Mak, “Mining sentiments in SMS texts for teaching evaluation,” Expert Systems with Applications, vol. 39, no. 3, pp. 2584–2589, Feb. 2012, doi: 10.1016/j.eswa.2011.08.113.

B. Jagtap and V. Dhotre, “SVM & HMM based hybrid approach of sentiment analysis for teacher feedback assessment,” International Journal of Emerging Trends & Technology in Computer Science, vol. 3, no. 3, pp. 229–232, 2014.

J. Ogden and J. Lo, “How meaningful are data from Kim, S. M. & Calvo, R. A. (2010). Sentiment analysis in student experiences of learning,” in Proceedings of the 3rd International Conference on Educational Data Mining, Pittsburgh, Pa, USA, 2012.

W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, Dec. 2014, doi: 10.1016/j.asej.2014.04.011.

A. Minanovic, H. Gabelica, and Z. Krstic, “Big data and sentiment analysis using KNIME: Online reviews vs. social media,” in 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, May 2014, pp. 1464–1468, doi: 10.1109/MIPRO.2014.6859797.

L. Augustyniak, T. Kajdanowicz, P. Kazienko, M. Kulisiewicz, and W. Tuliglowicz, “An Approach to Sentiment Analysis of Movie Reviews: Lexicon Based vs. Classification,” in Hybrid Artificial Intelligence Systems, Salamanca, Spain, Jun. 2014, vol. 8480, pp. 168–178, doi: 10.1007/978-3-319-07617-1_15.

S. Rosenthal, A. Ritter, P. Nakov, and V. Stoyanov, “Semeval-2014 task 9: sentiment analysis in twitter,” presented at the 8th International Workshop on Semantic Evaluation, Dublin, Ireland, Aug. 2014, pp. 73–80.

H. Saif, M. Fernandez, Y. He, and H. Alani, “Evaluation datasets for twitter sentiment analysis,” presented at the 1st International Workshop on Emotion and Sentiment in Social and Expressive Media, Torino, Italy, Dec. 2013.

N. Altrabsheh, M. M. Gaber, and M. Cocea, “SA-E: Sentiment Analysis for Education,” in Frontiers in Artificial Intelligence and Applications, vol. 255, 2013, pp. 353–362.

S. Rani and P. Kumar, “A Sentiment Analysis System to Improve Teaching and Learning,” Computer, vol. 50, no. 5, pp. 36–43, May 2017, doi: 10.1109/MC.2017.133.

A. M. Ramadhani and H. S. Goo, “Twitter sentiment analysis using deep learning methods,” in 7th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia, Aug. 2017, pp. 1–4, doi: 10.1109/INAES.2017.8068556.

C. Liu, W. Hsaio, C. Lee, G. Lu, and E. Jou, “Movie Rating and Review Summarization in Mobile Environment,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 3, pp. 397–407, May 2012, doi: 10.1109/TSMCC.2011.2136334.

P. Vateekul and T. Koomsubha, “A study of sentiment analysis using deep learning techniques on Thai Twitter data,” in 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), Khon Kaen, Thailand, Jul. 2016, pp. 1–6, doi: 10.1109/JCSSE.2016.7748849.

T. Li Im, P. Wai San, C. Kim On, R. Alfred, and P. Anthony, “Analysing market sentiment in financial news using lexical approach,” in IEEE Conference on Open Systems (ICOS), Kuching, Malaysia, Dec. 2013, pp. 145–149, doi: 10.1109/ICOS.2013.6735064.

G. Li and F. Liu, “A clustering-based approach on sentiment analysis,” in IEEE International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, China, Nov. 2010, pp. 331–337, doi: 10.1109/ISKE.2010.5680859.

N. Altrabsheh, E. Haig, and S. Fallahkhair, “Learning sentiment from students’ feedback for real-time interventions in classrooms,” in Adaptive and Intelligent Systems, Bournemouth, UK: Springer, 2014.

A. Ortigosa, J. M. Martin, and R. M. Carro, “Sentiment analysis in Facebook and its application to e-learning,” Computers in Human Behavior, vol. 31, pp. 527–541, Feb. 2014, doi: 10.1016/j.chb.2013.05.024.

C. Pong-Inwong and W. S. Rungworawut, “Teaching Senti-Lexicon for Automated Sentiment Polarity Definition in Teaching Evaluation,” in 10th International Conference on Semantics, Knowledge and Grids, Beijing, China, Aug. 2014, pp. 84–91, doi: 10.1109/SKG.2014.25.

P. Kaewyong, A. Sukprasert, N. Salim, and F. A. Phang, “The possibility of students’ comments automatic interpret using lexicon based sentiment analysis to teacher evaluation,” presented at the 3rd International Conference on Artificial Intelligence and Computer Science, Penang, Malaysia, Oct. 2015, pp. 179–189.

F. Colace, M. De Santo, and L. Greco, “SAFE: A Sentiment Analysis Framework for E-Learning,” International Journal of Emerging Technologies in Learning (iJET), vol. 9, no. 6, pp. 37–41, Dec. 2014, doi: 10.3991/ijet.v9i6.4110.

I. Ali, M. Chhajro, K. Kumar, H. Lashari, and U. Khan, “Student Feedback Sentiment Analysis Model Using Various Machine Learning Schemes A Review,” Indian Journal of Science and Technology, vol. 14, no. 12, pp. 1–9, Apr. 2019, doi: 10.17485/ijst/2019/v12i14/143243.

I. A. Kandhro, M. A. Chhajro, K. Kumar, H. N. Lashari, and U. Khan, “Student Feedback Sentiment Analysis Model using Various Machine Learning Schemes: A Review,” Indian Journal of Science and Technology, vol. 12, no. 14, Apr. 2019, doi: 10.17485/ijst/2019/v12i14/143243.

J. Islam and Y. Zhang, “Visual Sentiment Analysis for Social Images Using Transfer Learning Approach,” in IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), Atlanta, GA, USA, Oct. 2016, pp. 124–130, doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.29.

A. Severyn and A. Moschitti, “Twitter Sentiment Analysis with Deep Convolutional Neural Networks,” presented at the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, Aug. 2015.

L. Yanmei and C. Yuda, “Research on Chinese Micro-Blog Sentiment Analysis Based on Deep Learning,” in 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, Dec. 2015, vol. 1, pp. 358–361, doi: 10.1109/ISCID.2015.217.

J. Mir, M. Azhar, and S. Khatoon, “Aspect Βased Classification Model for Social Reviews,” Engineering, Technology and Applied Science Research, vol. 7, no. 6, pp. 2296–2302, Dec. 2017.

M. Madhukar and S. Verma, “Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India,” Engineering, Technology and Applied Science Research, vol. 7, no. 5, pp. 2014–2016, 2017.

Metrics

Abstract Views: 74
PDF Downloads: 40

Metrics Information
Bookmark and Share