Sentimental analysis using machine learning in Twitter dataset

Research Article
Open access

Sentimental analysis using machine learning in Twitter dataset

C. N. Vanitha 1* , S. Malathy 2 , S. A. Krishna 3 , M. Vanitha 4 , Sathishkumar V E. 5
  • 1 Department of Computer Science and Engineering, Kongu Engineering College, India    
  • 2 Department of Computer Science and Engineering, Kongu Engineering College, India    
  • 3 Department of Mechatronics Engineering, Kongu Engineering College, India    
  • 4 Department of Computer Technology, Kongu Engineering College, India    
  • 5 Department of Software Engineering, Jeonbuk National University, Jeonju, South Korea    
  • *corresponding author drcnvanitha@gmail.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230966
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

The analysis of sentiment is also known as opinion mining. Observation of sentiment is used for detecting different emotions of people through the feedback given by them. It is done to know whether the customer is satisfied by the organization’s product, service and so on. Nowadays rating the product or service becomes very essential and important in everyone’s life. These are nothing without rating the sentiments of the customer. It is absolutely essential to collect the sentimental data since it helps in improving the product or its service, to satisfy the customer and to increase the sale. In this fast-changing world, Twitter is one of the most used and biggest sensation creating apps. Twitter is mostly used for delivering thoughts of people. This process is known as sentimental delivery. This research analysis is done with the help of views, likes, comments and shares of a particular tweet. The output of this analysis might be positive, negative or neutral. Machine learning is an algorithm or a method in which the task is conducted by an AI system. In this output value is predicted from the given input data.

Keywords:

Sentimental analysis, emotions, product, rating, twitter data, Machine learning

Vanitha,C.N.;Malathy,S.;Krishna,S.A.;Vanitha,M.;E.,S.V. (2023). Sentimental analysis using machine learning in Twitter dataset. Applied and Computational Engineering,6,814-819.
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References

[1]. Ruz, G. A., Henríquez, P. A., & Mascareño, A. (2020). Future Generation Computer

[2]. Systems, 106, 92-104. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers.

[3]. Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., & Karthick, G. (2021). Wireless

[4]. Personal Communications, 1-10. Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM).

[5]. Kausar, M. A., Soosaimanickam, A., & Nasar, M. (2021). International Journal of Advanced Computer Science and Applications, 12(2). Public sentiment analysis on Twitter data during COVID-19 outbreak.

[6]. Malathy, S., Vanitha, C. N., Syamraj, V., Sudharsan, P., & Mohankkanth, E. (2022, May). In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 437-442). IEEE. Rainfall Prediction for Enhancing Crop-Yield based on Machine Learning Techniques.

[7]. Neogi, A. S., Garg, K. A., Mishra, R. K., & Dwivedi, Y. K. (2021). International Journal of Information Management Data Insights, 1(2), 100019. Sentiment analysis and classification of Indian farmers’ protest using twitter data.

[8]. Shamrat, F. M. J. M., Chakraborty, S., Imran, M. M., Muna, J. N., Billah, M. M., Das, P., & Rahman, O. M. (2021). Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 463-470. Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm.

[9]. AlBadani, B., Shi, R., & Dong, J. (2022). Applied System Innovation, 5(1), 13. A novel machine learning approach for sentiment analysis on Twitter incorporating the universal language model fine-tuning and SVM.

[10]. Jabalameli, S., Xu, Y., & Shetty, S. (2022).International Journal of Disaster Risk Reduction, 80, 103204. Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: At the early stage of vaccination.

[11]. Bose, D., Aithal, P. S., & Roy, S. (2021).International Journal of Management, Technology, and Social Sciences (IJMTS), 6(1), 110-127. Survey of Twitter Viewpoint on Application of Drugs by VADER Sentiment Analysis among Distinct Countries.

[12]. Singh, C., Imam, T., Wibowo, S., & Grandhi, S. (2022). Applied Sciences, 12(8), 3709. A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews.

[13]. Vanitha, C. N., Malathy, S., Shenbagavalli, P., Krishna, S. A., & Kavin, K. (2022, May). In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 431-436). IEEE. Detecting Turmeric Taphrina Maculans Disease using Machine Learning Algorithms.

[14]. Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020).Journal of medical Internet research, 22(11), e20550. Twitter discussions and emotions about the COVID-19 pandemic: Machine learning approach.

[15]. Vanitha, C. N. (2021, December). In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1-5). IEEE. Diabetes Disease Prediction Using Artificial Neural Network with Machine Learning Approaches.

[16]. Shanmugavadivel, K., Sathishkumar, V. E., Raja, S., Lingaiah, T. B., Neelakandan, S., & Subramanian, M. (2022). Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data. Scientific Reports, 12(1), 1-12

Cite this article

Vanitha,C.N.;Malathy,S.;Krishna,S.A.;Vanitha,M.;E.,S.V. (2023). Sentimental analysis using machine learning in Twitter dataset. Applied and Computational Engineering,6,814-819.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ruz, G. A., Henríquez, P. A., & Mascareño, A. (2020). Future Generation Computer

[2]. Systems, 106, 92-104. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers.

[3]. Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., & Karthick, G. (2021). Wireless

[4]. Personal Communications, 1-10. Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM).

[5]. Kausar, M. A., Soosaimanickam, A., & Nasar, M. (2021). International Journal of Advanced Computer Science and Applications, 12(2). Public sentiment analysis on Twitter data during COVID-19 outbreak.

[6]. Malathy, S., Vanitha, C. N., Syamraj, V., Sudharsan, P., & Mohankkanth, E. (2022, May). In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 437-442). IEEE. Rainfall Prediction for Enhancing Crop-Yield based on Machine Learning Techniques.

[7]. Neogi, A. S., Garg, K. A., Mishra, R. K., & Dwivedi, Y. K. (2021). International Journal of Information Management Data Insights, 1(2), 100019. Sentiment analysis and classification of Indian farmers’ protest using twitter data.

[8]. Shamrat, F. M. J. M., Chakraborty, S., Imran, M. M., Muna, J. N., Billah, M. M., Das, P., & Rahman, O. M. (2021). Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 463-470. Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm.

[9]. AlBadani, B., Shi, R., & Dong, J. (2022). Applied System Innovation, 5(1), 13. A novel machine learning approach for sentiment analysis on Twitter incorporating the universal language model fine-tuning and SVM.

[10]. Jabalameli, S., Xu, Y., & Shetty, S. (2022).International Journal of Disaster Risk Reduction, 80, 103204. Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: At the early stage of vaccination.

[11]. Bose, D., Aithal, P. S., & Roy, S. (2021).International Journal of Management, Technology, and Social Sciences (IJMTS), 6(1), 110-127. Survey of Twitter Viewpoint on Application of Drugs by VADER Sentiment Analysis among Distinct Countries.

[12]. Singh, C., Imam, T., Wibowo, S., & Grandhi, S. (2022). Applied Sciences, 12(8), 3709. A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews.

[13]. Vanitha, C. N., Malathy, S., Shenbagavalli, P., Krishna, S. A., & Kavin, K. (2022, May). In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 431-436). IEEE. Detecting Turmeric Taphrina Maculans Disease using Machine Learning Algorithms.

[14]. Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020).Journal of medical Internet research, 22(11), e20550. Twitter discussions and emotions about the COVID-19 pandemic: Machine learning approach.

[15]. Vanitha, C. N. (2021, December). In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1-5). IEEE. Diabetes Disease Prediction Using Artificial Neural Network with Machine Learning Approaches.

[16]. Shanmugavadivel, K., Sathishkumar, V. E., Raja, S., Lingaiah, T. B., Neelakandan, S., & Subramanian, M. (2022). Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data. Scientific Reports, 12(1), 1-12

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