Shaker, K. (2022). Optimizing Sentiment Big Data Classification Using Multilayer Perceptron. , 13(2), 14-21. doi: 10.37649/aengs.2022.176353
Khalid Shaker. "Optimizing Sentiment Big Data Classification Using Multilayer Perceptron". , 13, 2, 2022, 14-21. doi: 10.37649/aengs.2022.176353
Shaker, K. (2022). 'Optimizing Sentiment Big Data Classification Using Multilayer Perceptron', , 13(2), pp. 14-21. doi: 10.37649/aengs.2022.176353
Shaker, K. Optimizing Sentiment Big Data Classification Using Multilayer Perceptron. , 2022; 13(2): 14-21. doi: 10.37649/aengs.2022.176353

Optimizing Sentiment Big Data Classification Using Multilayer Perceptron

Anbar Journal of Engineering Sciences
Article 2, Volume 13, Issue 2, Autumn 2022, Page 14-21  XML PDF (856 K)
Document Type: Research Paper
DOI: 10.37649/aengs.2022.176353
Author
Khalid Shaker orcid
College of Computer Science and IT, University of Anbar
Abstract
Internet-based platforms such as social media have a great deal of big data that is available in the shape of text, audio, video, and image. Sentiment Analysis (SA) of this big data has become a field of computational studies. Therefore, SA is necessary in texts in the form of messages or posts to determine whether a sentiment is negative or positive. SA is also crucial for the development of opinion mining systems. SA combines techniques of Natural Language Processing (NLP) with data mining approaches for developing inelegant systems. Therefore, an approach that can classify sentiments into two classes, namely, positive sentiment and negative sentiment is proposed. A Multilayer Perceptron (MLP) classifier has been used in this document classification system. The present research aims to provide an effective approach to improving the accuracy of SA systems. The proposed approach is applied to and tested on two datasets, namely, a Twitter dataset and a movie review dataset; the accuracies achieved reach 85% and 99% respectively.
Keywords
electrical power; optimum inclination angle; PV/T collector; thermal efficiency
References
[1]       K. Francisco and M. Panguila, “Sentiment Analysis on Social Media Data Using Intelligent Techniques,” International Journal of Engineering Research and Technology, 2019.

[2]       S. Joshi and D. Deshpande, “Twitter Sentiment Analysis System,” Int. J. Comput. Appl., vol. 180, no. 47, pp. 35–39, 2018.

[3]       A. Kumar and G. Garg, “Systematic literature review on context-based sentiment analysis in social multimedia,” Multimed. Tools App., 2019.

[4]       M. Amajd, Z. Kaimuldenov, and I. Voronkov, “Text Classification with Deep Neural Networks,” International Conference on Actual Problems of System and Software Engineering, pp. 364-370,2017.

[5]       V. VardanReddy, M. Maila, S. Sri Raghava, Y. Avvaru, and S. V. Koteswarao, “A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., pp. 537–542, 2019.

[6]       L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 8, no. 4, Jul. 2018.

[7]       B. Lopez and X. Sumba, “IMDb Sentiment Analysis,” pp. 2–6, 2019.

[8]       R. Adyan Marendra, and H. S. Goo, "Twitter sentiment analysis using deep learning methods," 2017 IEEE 7th International Annual Engineering Seminar (InAES), pp. 1-4, 2017.‏

[9]       V. N. Patodkar and I. R. Shaikh, “Twitter as a Corpus for Sentimental Analysis using Naïve Bayes,” vol. 6, no. 7, pp. 375–379, 2017.

[10]    K. K. Uma and D. K. Meenakshisundaram, “A Novel Optimizer Technique for Sentiment Polarity in Social Web Environment,” International Journal of Applied Engineering Research, pp. 118–125, 2019.

[11]    B. Duncan and Y. Zhang, “Neural networks for sentiment analysis on Twitter,” Proc. 2015 IEEE 14th Int. Conf. Cogn. Informatics Cogn. Comput. ICCI*CC 2015, pp. 275–278, 2015.

[12]    A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of Sentimental Reviews Using Machine Learning Techniques,” Procedia Comput. Sci., vol. 57, pp. 821–829, 2015.

[13]          M. Z. Asghar, S. Ahmad, M. Qasim, S. R. Zahra, and F. M. Kundi, “SentiHealth: creating health-related sentiment lexicon using hybrid approach,” Springerplus, vol. 5, no. 1, 2016.

[14]    R. A. Ramadhani, F. Indriani, and D. T. Nugrahadi, “Comparison of Naive Bayes smoothing methods for Twitter sentiment analysis,” 2016 Int. Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2016, pp. 287–292, 2017.

[15]    S. Mehla, and M. M “Sentiment Analysis of Movie Reviews using Machine Learning Classifiers,” Int. J. Comput. Appl., vol. 182, no. 50, pp. 25–28, 2019.

[16]    H. Han, X. Bai, and P. Li, “Augmented sentiment representation by learning context information,” Neural Comput. Appl., vol. 4, 2018.

[17]    B. Ay Karakuş, M. Talo, İ. R. Hallaç, and G. Aydin, “Evaluating deep learning models for sentiment classification,” Concurr. Comput, vol. 30, no. 21, pp. 1–14, 2018.

[18]    D. M. Reddy, D. N. V. S. Reddy, and D. N. V. S. Reddy, “Twitter Sentiment Analysis using Distributed Word and Sentence Representation,” arXiv preprint arXiv:1904.12580, 2019.

[19]    N. Mohamed Ali, M. M. A. El Hamid, and A. Youssif, “Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models,” Int. J. Data Min. Knowl. Manag. Process, vol. 09, no. 03, pp. 19–27, 2019.

[20]    X. Li, P. Wu, W Wang “Incorporating stock prices and news sentiments for stock market

 

[21]    prediction,”Inf Process Manag 57(5):102212. https ://doi.org/10.1016/j.ipm.2020.10221, 2020.

 

[22]    U. D. Gandhi, P. Malarvizhi Kumar, G. Chandra Babu, and G. Karthick, “Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM),” Wirel. Pers. Commun., no. 0123456789, 2021.

[23]    M. Mahyarani, A. Adiwijaya, S. Al Faraby, & M. Dwifebri, “Implementation of Sentiment Analysis Movie Review based on IMDB with Naive Bayes Using Information Gain on Feature Selection,” In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA) (pp. 99-103). IEEE, 2021

[24]    M. Pota, M. Ventura, R. Catelli, M. Esposito, “An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian,” Sensors, 21, 133, 2021.

[25]    B. AlBadani, R. Shi, J. Dong, “A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM,” Appl. Syst. Innov. , 5, 13, 2022.

 

[26]    T. Swathi, N. Kasiviswanath, & A. Rao, “An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis,” Appl Intell, 2022.

[27]    Kaggle website, https://www.kaggle.com/  [Online], accessed on  March, 2022.

[28]    IMDB review, http://reviews.imdb.com/Review/ [Online], accessed on March, 2022.

[29]    Natural Language Toolkit, https://www.nltk.org/ [Online], accessed on March, 2022

[30]    A., Alqudsi, N. Omar, & Ibrahim, R. W. (2016). Rule Based and Expectation Maximization algorithm for Arabic-English Hybrid Machine Translation. International Journal of Artificial Intelligence (IJ-AI), 5.

[31]    A., Alqudsi, N. Omar, & Shaker, K. (2014). Arabic machine translation: a survey. Artificial Intelligence Review, 42(4), 549-572.

[32]   Alqudsi, A., Omar, N., & Shaker, K. (2019, May). A hybrid rules and statistical method for Arabic to English machine translation. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-7). IEEE. 

Statistics
Article View: 58
PDF Download: 57