An Ensembled Based Machine Learning Technique of Sentiment Analysis
Keywords:Sentiment Analysis, Machine Learning, Ensemble, Social Networking
User evaluations on social networking sites such as Twitter, YouTube, and Facebook have grown rapidly due to their widespread use in sentiment analysis, providing valuable insight for both governmental and non-governmental organizations. Analyzing these evaluations not only helps improve the effectiveness of products and services, but also aids in the developing operational and management strategies. Although various analysis models have been proposed, challenges remain in processing, classifying, and accessing user evaluations, such as dealing with complex sentences, requiring more than sentiment words and achieving adequate accuracy and performance based on limited labeled data. This study primarily examines the performance of three commonly used machine learning algorithms proposes an Ensemble method, which combines Naive Bayes, Support Vector Machines and K-nearest Neihbor algorithms. The proposed method was tested on a Twitter dataset.The Ensemble method creates a classification model by applying the three classification algorithms: Naive Bayes, Support Vector Machines and K-nearest Neighbor, to for the prediction of unknown example and assigns the predicted class receiving the most votes. According to the results, the Ensemble method has an accuracy of 83.28% at 60/40 test split, 83.27% at 70/30 test split, 83.50% at 80/20 test split, and 86.12% at 90/10 test split, and F-measure of 84.15% at 60/40 test split, 83.72% at 70/30 test split, 84.41% at 80/20 test split, and 86.58% at 90/10 test split. In terms of individual performance, k-nearest Neighbor has better accuracy and F-measure than Support Vector Machine and Naive Bayes while the Ensemble method proves to be the most efficient in terms of accuracy and F-measure.
K. Ravi and V. J. K.-b. s. Ravi, "A survey on opinion mining and sentiment analysis: tasks, approaches and applications," Knowledge-based systems, vol. 89, pp. 14-46, 2015.
D. Höttecke and D. J. S. E. Allchin, "Reconceptualizing nature‐of‐science education in the age of social media," Science Education, vol. 104, no. 4, pp. 641-666, 2020.
C. I. Papanagnou, O. J. C. Matthews-Amune, and O. Research, "Coping with demand volatility in retail pharmacies with the aid of big data exploration," Computers & Operations Research, vol. 98, pp. 343-354, 2018.
D. Gurkhe, N. Pal, and R. J. I. J. o. C. A. Bhatia, "Effective sentiment analysis of social media datasets using Naive Bayesian classification," International Journal of Computer Applications, vol. 975, no. 8887, p. 99, 2014.
S. Sohangir, D. Wang, A. Pomeranets, and T. M. J. J. o. B. D. Khoshgoftaar, "Big Data: Deep Learning for financial sentiment analysis," Journal of Big Data, vol. 5, no. 1, pp. 1-25, 2018.
S. Siddiqui, T. J. I. j. o. c. a. t. Singh, and research, "Social media its impact with positive and negative aspects," journal of computer applications technology and research, vol. 5, no. 2, pp. 71-75, 2016.
S. F. Waterloo, S. E. Baumgartner, J. Peter, P. M. J. n. m. Valkenburg, and society, "Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp," New Media & Society, vol. 20, no. 5, pp. 1813-1831, 2018.
A. Giachanou and F. J. A. C. S. Crestani, "Like it or not: A survey of twitter sentiment analysis methods," ACM Computing Surveys (CSUR), vol. 49, no. 2, pp. 1-41, 2016.
H. Ismail, S. Harous, and B. J. R. C. S. Belkhouche, "A Comparative Analysis of Machine Learning Classifiers for Twitter Sentiment Analysis," Res. Comput. Sci., vol. 110, pp. 71-83, 2016.
M. M. CHANDIO, "TWITTER SENTIMENTS ANALYSIS," PhD diss., NEAR EAST UNIVERSITY, 2019.
A. Alsaeedi, M. Z. J. I. J. o. A. C. S. Khan, and Applications, "A study on sentiment analysis techniques of Twitter data," International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, pp. 361-374, 2019.
M. Čišija, E. Žunić, and D. Đonko, "Collection and sentiment analysis of twitter data on the political atmosphere," in 2018 14th symposium on neural networks and applications (NEUREL), 2018, pp. 1-5: IEEE.
S. t. S. Avudaiappan and T. J. I. J. O. S. T. R. V. Jenifer, ISSUE 02, "Twitter sentimental analysis using neural network," INTERNATIONAL JOURNAL OF SCIENTIFIC TECHNOLOGY RESEARCH , vo/. 9, pp. 2277-8616, 2020.
M. Tabra, A. J. T. I. J. o. E.-L. Lawan, and E. T. i. t. D. Media, "A Comparative Analysis of the Performance of Three Machine Learning Algorithms for Tweets on Nigerian dataset," The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM), vol. 3, no. 1, pp. 23-30, 2017.
A. Telukdarie, E. Buhulaiga, S. Bag, S. Gupta, Z. J. P. S. Luo, and E. Protection, "Industry 4.0 implementation for multinationals," Process Safety and Environmental Protection, vol. 118, pp. 316-329, 2018.
N. J. P. c. s. Saleena, "An ensemble classification system for twitter sentiment analysis," Procedia computer science, vol. 132, pp. 937-946, 2018.
S. Ernawati and E. R. Yulia, "Implementation of the Naïve Bayes algorithm with feature selection using genetic algorithm for sentiment review analysis of fashion online companies," in 2018 6th International Conference on Cyber and IT Service Management (CITSM), 2018, pp. 1-5: IEEE.
S. S. Hanswal, A. Pareek, and A. J. I. J. o. C. A. Sharma, "Twitter Sentiment Analysis using Rapid Miner Tool," International Journal of Computer Applications, vol. 975, p. 8887.
H. Wisnu, M. Afif, and Y. Ruldevyani, "Sentiment analysis on customer satisfaction of digital payment in Indonesia: A comparative study using KNN and Naïve Bayes," in Journal of Physics: Conference Series, 2020, vol. 1444, no. 1, p. 012034: IOP Publishing.
M. Birjali, M. Kasri, and A. J. K.-B. S. Beni-Hssane, "A comprehensive survey on sentiment analysis: Approaches, challenges and trends," Knowledge-Based Systems, vol. 226, p. 107134, 2021.
K. Korovkinas, P. Danėnas, and G. J. B. j. o. m. c. Garšva, "SVM and Naïve Bayes classification ensemble method for sentiment analysis," Baltic journal of modern computing, vol. 5, no. 4, pp. 398-409, 2017.
A. Poornima and K. S. Priya, "A comparative sentiment analysis of sentence embedding using machine learning techniques," in 2020 6th international conference on advanced computing and communication systems (ICACCS), 2020, pp. 493-496: IEEE.
L. Yue, W. Chen, X. Li, W. Zuo, M. J. K. Yin, and I. Systems, "A survey of sentiment analysis in social media," Knowledge and Information Systems, vol. 60, no. 2, pp. 617-663, 2019.
Y. Al Amrani, M. Lazaar, and K. E. J. P. C. S. El Kadiri, "Random forest and support vector machine based hybrid approach to sentiment analysis," Procedia Computer Science, vol. 127, pp. 511-520, 2018.
N. O. F. Daeli, A. J. J. o. D. S. Adiwijaya, and I. Applications, "Sentiment analysis on movie reviews using Information gain and K-nearest neighbor," Journal of Data Science and Its Applications, vol. 3, no. 1, pp. 1-7, 2020.
J. Pallant, SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge, 2020.
W. J. I. o. S. S. B. Ahmed, "Using Twitter as a data source:
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