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Performance Analysis of Machine Learning and Deep Learning Algorithms for Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 44 |
| Year of Publication: 2021 |
| Authors: Mugdha Deokar, Varun Godse |
10.5120/ijca2021921854
|
Mugdha Deokar, Varun Godse . Performance Analysis of Machine Learning and Deep Learning Algorithms for Sentiment Analysis. International Journal of Computer Applications. 183, 44 ( Dec 2021), 30-34. DOI=10.5120/ijca2021921854
Abstract
Public opinion or review of a product, a movie, or a restaurant is a key driver of trends and influences how a person chooses a particular service. This out-pour of opinion contributes to the overall information and becomes a crucial tool to analyze the sentiment towards the service provider. A large pool of information thus can be processed with the help of Natural Language Processing (NLP) tools to find out how good or bad a movie, product, or restaurant is. A dataset containing sentences from websites like Amazon, IMDb, and Yelp to study and predict people's sentiment towards a particular service is used. To achieve the results, Natural Language Processing for sentiment analysis is utilized. Initial step of the project includes designing a model based on six machine learning algorithms like Support Vector Machine, Random Forest, and other algorithms for classification purposes. Next step was using a voting classifier to extract the best features of each algorithm and get a conclusive result. Further, Recurrent Neural Networks and Long Short Term Memory(LSTMs) leverage the power of deep learning to achieve higher accuracy and better results. Also, the BERT model is used to perform sentiment analysis. Thus, the paper aims to compare various possible algorithms that can be used for sentiment analysis using machine learning, deep learning, and natural language processing. In the end, it is investigated if the public response to the service provided is either positive or negative.
References
- M. R. Hasan, M. Maliha and M. Arifuzzaman, "Sentiment Analysis with NLP on Twitter Data," 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), 2019, pp. 1-4, doi: 10.1109/IC4ME247184.2019.9036670.
- https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270 (Accessed on 28 Aug 2021)
- M. Kanakaraj and R. M. R. Guddeti, "Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques," Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015, pp. 169-170, doi: 10.1109/ICOSC.2015.7050801.
- Y. A. Solangi, Z. A. Solangi, S. Aarain, A. Abro, G. A. Mallah and A. Shah, "Review on Natural Language Processing (NLP) and Its Toolkits for Opinion Mining and Sentiment Analysis," 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), 2018, pp. 1-4, doi: 10.1109/ICETAS.2018.8629198.
- J. Hu, X. Kang, S. Nishide and F. Ren, "Text multi-label sentiment analysis based on Bi-LSTM," 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), 2019, pp. 16-20, doi: 10.1109/CCIS48116.2019.9073727.
- J. Zheng and L. Zheng, "A Dictionary-Based Convolutional Recurrent Neural Network Model for Sentiment Analysis," 2019 International Conference on Communications, Information System and Computer Engineering (CISCE), 2019, pp. 606-611, doi: 10.1109/CISCE.2019.00142.
- F. Ceci, A. L. Gonçalves and R. Weber, "A model for sentiment analysis based on ontology and cases," in IEEE Latin America Transactions, vol. 14, no. 11, pp. 4560-4566, Nov. 2016, doi: 10.1109/TLA.2016.7795829.
- Serrano-Guerrero, Jesus & Olivas, José & Romero, Francisco & Herrera-Viedma, Enrique. (2015). Sentiment analysis: A review and comparative analysis of web services. Information Sciences. 311. , 18–38. 10.1016/j.ins.2015.03.040.
- E. Cambria, "Affective Computing and Sentiment Analysis," in IEEE Intelligent Systems, vol. 31, no. 2, pp. 102-107, Mar.-Apr. 2016, doi: 10.1109/MIS.2016.31
- Shiliang Sun, Chen Luo, Junyu Chen,A review of natural language processing techniques for opinion mining systems,InformationFusion,Volume 36,2017,Pages 10-25,ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2016.10.004.
- H. S. and R. Ramathmika, "Sentiment Analysis of Yelp Reviews by Machine Learning," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, pp. 700-704, doi: 10.1109/ICCS45141.2019.9065812.
- A. J. Nair, V. G and A. Vinayak, "Comparative study of Twitter Sentiment On COVID - 19 Tweets," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021, pp. 1773-1778, doi: 10.1109/ICCMC51019.2021.9418320.
- Xu, Baojun& Huang, Joshua & Williams, Graham & Wang, Qiang& Ye, Yunming. Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces. International Journal of Data Warehousing and Mining, 8(2), 44-63, 2012.
- Joachims, Thorsten. "Text categorization with support vector machines: learning with many relevant features." Paper presented at the meeting of the Proceedings of ECML-98, 10th European Conference on Machine Learning, Chemnitz, DE, 1998.
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