Sentiment Analysis using various Machine Learning and Deep Learning Techniques


  • V Umarani Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India
  • A Julian Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India
  • J Deepa Department of Information Technology, VelTech Institute of Technology, Chennai, Tamil Nadu, India


Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.


P. Poomka, N. Kerdprasop & K. Kerdprasop, “Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews” International Journal of Machine Learning and Computing 11 (2021) 103, doi: 10.18178/ijmlc.2021.11.2.1021.

K. Klimiuk, A. Czoska, K. Biernacka & L. Balwicki, “Vaccine Misinformation on Social Media–Topic-Based Content and Sentiment Analysis of Polish Vaccine-Deniers Comments on Face book”, Human Vaccines & Immunotherapeutic 17 (2021) 2026.

H. Tsaniya, R. Rosadi & A. S. Abdullah, “Sentiment Analysis towards Jokowis Government Using Twitter Data with Convolutional Neural Network Method”, Journal of Physics Conference Series, 1722 (2021) 012017,doi:10.1088/1742-6596/1722/1/012017.

R. DEndsuy, “Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets”, Journal of Applied Data Sciences 2 (2021) 8.

M. Bibi, W. Aziz, M. Almaraashi, I. H. Khan, M. S. A. Nadeem & N. Habib, “A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis”, IEEE Access 8 (2020) 68580.

R. Cekik & S. Telceken, “A New Classification Method Based on Rough Sets Theory”, Soft Computing 6 (2018) 1881.

B. Peng, J. Wang & X. Zhang, “Adversarial Learning of Sentiment WordRepresentations for Sentiment Analysis”, Information Sciences 541 (2020) 426.

X. Tan, Y. Cai, J. Xu, H. F Leung, W. Chen & Q. Li, “Improving AspectBased Sentiment Analysis via Aligning Aspect Embedding”, Neuro computing 383 (2020) 336.

A. Jain & V. Jain, “Sentiment Classification Using Hybrid Feature Selection and Ensemble Classifier” Journal of Intelligent & Fuzzy Systems, 4(2021) 221.

P. Kalaivani & K. L. Shunmuganathan, “Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches”, Indian Journal of Computer Science and Engineering 4 (2013) 285.

M.Ghosh&G.Sanyal, “Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis”, Applied Computational Intelligence and Soft Computing 2018 (2018) 10.

A. P. Rodrigues & N. N. Chiplunkar, “A New Big Data Approach for Topic Classification and Sentiment Analysis of Twitter Data”, Evolutionary Intelligence 2 (2019)11.

Z. Jianqiang, G. Xiaolin & Z. Xuejun, “Deep Convolution Neural Networks for Twitter Sentiment Analysis”, IEEE Access 6 (2018) 23253.

W. Li, L. Zhu, Y. Shi, K. Guo, & E. Cambria, “User reviews: Sentiment analysis using lexicon integrated two-channel CNN–LSTMfamily models”, Applied Soft Computing 94 (2020)106435.

A. S. Imran, S. M. Daudpota, Z. Kastrati & R. Batra, “Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets”, IEEE Access 8 (2020) 181074.

S. Rani, N. S. Gill & P. Gulia, “Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data”, International journal of Advanced computer Science and applications 12 (2021) 222.

R. Cekik & A. K. Uysal, “A novel filter feature selection method using rough set for short text data”, Expert Systems with Applications 160 (2020) 113691.

I. S. Ahma, A. B. Azuraliza & M. R. Yaakub, “A review of feature selection in sentiment analysis using information gain and domain specific ontology”, International Journal of Advanced Computer Research 9 (2019) 283.

C. Albon , “Machine Learning with python cook book : Practical solutions from preprocessing to deep learning”, OReilly media (2018) 366.

Z. Wu&S.King,“Investigating gated recurrent networks for speech synthesis”, IEEE International Conference on Acoustics, Speech and Signal Processing (2016) 5140.

B. Peng, J. Wang & X. Zhang, “Adversarial learning of sentiment word representations for sentiment analysis”, Information Sciences 541 (2020) 426.

Z.Jianqiang, G. Xiaolin & Z. Xuejun, “Deep convolution neural networks for twitter sentiment analysis” , IEEE Access 6 (2018)23253.

N. Isnaini, M. S. Mubarok & M. Y. A. Bakar, “A multi-label classification on topics of Indonesian news using K-Nearest Neighbor”, Journal of Physics: Conference Series 1192(2019) 012027.

T. Anuprathibha & C. S. KanimozhiSelvi, “Enhanced Medical Tweet Opinion Mining using Improved Dolphin Echolocation Algorithm Based Feature Selection”, International journal of Innovative Technology and Exploring engineering 2(2019)20.

H. Zikang, Y. Yong, Y. Guofeng & Z. Xinyu, “Sentiment analysis of agricultural product ecommerce review data based on deep learning”, International Conference on Internet of Things and Intelligent Applications, 27(2020) 7.

M. M. Ali, “Arabic sentiment analysis about online learning to mitigate covid-19”, Journal of Intelligent Systems 30(2021) 524.

U. Naseem, I. Razzak, M. Khushi, P. W. Eklund & J. Kim, “Covidsenti: Alarge-scale benchmark Twitter data set for COVID-19 sentiment analysis”, IEEE Transactions on Computational Social Systems 29(2021)175.

Z. Wang, H. Wang, Z. Liu & J. Liu, “Rolling Bearing Fault Diagnosis Using CNN-based Attention Modules and Gated Recurrent Unit”, Global Reliability and Prognostics and Health Management 7(2020) 6.

A. Vieira & W. Brandao, “Evaluating Acceptance of Video Games using Convolutional Neural Networks for Sentiment Analysis of User Reviews”, Proceedings of the 30th ACM Conference on Hypertext and Social Media 2(2019) 273.

K. Hirota & F. Masahiro, “Efficient Attention Mechanism by Softmax Function with Trained Coefficient”, IEICE Technical Report 339 (2021) 52.p



How to Cite

Sentiment Analysis using various Machine Learning and Deep Learning Techniques. (2021). Journal of the Nigerian Society of Physical Sciences, 3(4), 385-394.



Original Research

How to Cite

Sentiment Analysis using various Machine Learning and Deep Learning Techniques. (2021). Journal of the Nigerian Society of Physical Sciences, 3(4), 385-394.