SURVEY ON TWITTER SENTIMENT ANALYSIS USING MACHINE LEARNING
##plugins.themes.bootstrap3.article.main##
Abstract
Sentiment analysis manages distinguishing and classifying opinions or sentiments communicated in source message. Social networking sites like twitter have a large number of people share their contemplations step by step as tweets. As tweet is trademark short and fundamental method of articulation. The Sentiment Analysis sees as area of message data in Machine Learning. The exploration of sentiment analysis of Twitter data can be acted in various perspectives. This paper shows sentiment analysis types and methods used to perform extraction of sentiment from tweets. In this paper surveyed different Machine Learning techniques and approaches of sentiment analysis having twitter as a data.
##plugins.themes.bootstrap3.article.details##
Section
Articles
References
[1]. H. Elzayady, K. M. Badran and G. I. Salama, "Sentiment Analysis on Twitter Data using Apache Spark Framework," 2018 13th International Conference on Computer Engineering and Systems (ICCES), 2018, pp. 171-176, doi: 10.1109/ICCES.2018.8639195.
[2]. S. Dhawan, K. Singh and P. Chauhan, "Sentiment Analysis of Twitter Data in Online Social Network," 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC), 2019, pp. 255-259, doi: 10.1109/ISPCC48220.2019.8988450.
[3]. S. E. Saad and J. Yang, "Twitter Sentiment Analysis Based on Ordinal Regression," in IEEE Access, vol. 7, pp. 163677-163685, 2019, doi: 10.1109/ACCESS.2019.2952127.
[4]. L. Wang, J. Niu and S. Yu, "SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 10, pp. 2026-2039, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2913641.
[5]. E. D. Sri Mulyani, D. Rohpandi and F. A. Rahman, "Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia," 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Denpasar, Bali, Indonesia, 2019, pp. 89-93, doi: 10.1109/ICORIS.2019.8874896.
[6]. S. Saini, R. Punhani, R. Bathla and V. K. Shukla, "Sentiment Analysis on Twitter Data using R," 2019 International Conference on Automation, Computational and Technology Management (ICACTM), London, UK, 2019, pp. 68-72, doi: 10.1109/ICACTM.2019.8776685.
[7]. S. A. El Rahman, F. A. AlOtaibi and W. A. AlShehri, "Sentiment Analysis of Twitter Data," 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp. 1-4, doi: 10.1109/ICCISci.2019.8716464.
[8]. G. Shetty, A. Nair, P. Vishwanath and A. Stuti, "Sentiment Analysis and Classification on Twitter Spam Account Dataset," 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India, 2020, pp. 111-114, doi: 10.1109/ACCTHPA49271.2020.9213206.
[9]. F. W. Kurniawan and W. Maharani, "Indonesian Twitter Sentiment Analysis Using Word2Vec," 2020 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, 2020, pp. 1-6, doi: 10.1109/ICoDSA50139.2020.9212906.
[10]. J. Akaichi, "Social Networks' Facebook' Statutes Updates Mining for Sentiment Classification," 2013 International Conference on Social Computing, 2013, pp. 886-891, doi: 10.1109/SocialCom.2013.135.
[11]. M. S. Islam, M. A. Islam, M. A. Hossain and J. J. Dey, "Supervised approach of sentimentality extraction from bengali facebook status," 2016 19th International Conference on Computer and Information Technology (ICCIT), 2016, pp. 383-387, doi: 10.1109/ICCITECHN.2016.7860228.
[12]. H. M. S. Aung and W. P. Pa, "Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text," 2020 IEEE Conference on Computer Applications(ICCA), 2020, pp. 1-7, doi: 10.1109/ICCA49400.2020.9022842.
[13]. A. Elouardighi, M. Maghfour, H. Hammia and F. -z. Aazi, "A machine Learning approach for sentiment analysis in the standard or dialectal Arabic Facebook comments," 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), 2017, pp. 1-8, doi: 10.1109/CloudTech.2017.8284706.
[14]. Nahar KM, Jaradat A, Atoum MS, Ibrahim F. Sentiment analysis and classification of arab jordanian facebook comments for jordanian telecom companies using lexicon-based approach and machine learning. Jordanian Journal of Computers and Information Technology (JJCIT). 2020 Sep;6(03):52-71.
[15]. Ortigosa A, Martín JM, Carro RM. Sentiment analysis in Facebook and its application to e-learning. Computers in human behavior. 2014 Feb 1;31:527-41.
[2]. S. Dhawan, K. Singh and P. Chauhan, "Sentiment Analysis of Twitter Data in Online Social Network," 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC), 2019, pp. 255-259, doi: 10.1109/ISPCC48220.2019.8988450.
[3]. S. E. Saad and J. Yang, "Twitter Sentiment Analysis Based on Ordinal Regression," in IEEE Access, vol. 7, pp. 163677-163685, 2019, doi: 10.1109/ACCESS.2019.2952127.
[4]. L. Wang, J. Niu and S. Yu, "SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 10, pp. 2026-2039, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2913641.
[5]. E. D. Sri Mulyani, D. Rohpandi and F. A. Rahman, "Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia," 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Denpasar, Bali, Indonesia, 2019, pp. 89-93, doi: 10.1109/ICORIS.2019.8874896.
[6]. S. Saini, R. Punhani, R. Bathla and V. K. Shukla, "Sentiment Analysis on Twitter Data using R," 2019 International Conference on Automation, Computational and Technology Management (ICACTM), London, UK, 2019, pp. 68-72, doi: 10.1109/ICACTM.2019.8776685.
[7]. S. A. El Rahman, F. A. AlOtaibi and W. A. AlShehri, "Sentiment Analysis of Twitter Data," 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp. 1-4, doi: 10.1109/ICCISci.2019.8716464.
[8]. G. Shetty, A. Nair, P. Vishwanath and A. Stuti, "Sentiment Analysis and Classification on Twitter Spam Account Dataset," 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India, 2020, pp. 111-114, doi: 10.1109/ACCTHPA49271.2020.9213206.
[9]. F. W. Kurniawan and W. Maharani, "Indonesian Twitter Sentiment Analysis Using Word2Vec," 2020 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, 2020, pp. 1-6, doi: 10.1109/ICoDSA50139.2020.9212906.
[10]. J. Akaichi, "Social Networks' Facebook' Statutes Updates Mining for Sentiment Classification," 2013 International Conference on Social Computing, 2013, pp. 886-891, doi: 10.1109/SocialCom.2013.135.
[11]. M. S. Islam, M. A. Islam, M. A. Hossain and J. J. Dey, "Supervised approach of sentimentality extraction from bengali facebook status," 2016 19th International Conference on Computer and Information Technology (ICCIT), 2016, pp. 383-387, doi: 10.1109/ICCITECHN.2016.7860228.
[12]. H. M. S. Aung and W. P. Pa, "Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text," 2020 IEEE Conference on Computer Applications(ICCA), 2020, pp. 1-7, doi: 10.1109/ICCA49400.2020.9022842.
[13]. A. Elouardighi, M. Maghfour, H. Hammia and F. -z. Aazi, "A machine Learning approach for sentiment analysis in the standard or dialectal Arabic Facebook comments," 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), 2017, pp. 1-8, doi: 10.1109/CloudTech.2017.8284706.
[14]. Nahar KM, Jaradat A, Atoum MS, Ibrahim F. Sentiment analysis and classification of arab jordanian facebook comments for jordanian telecom companies using lexicon-based approach and machine learning. Jordanian Journal of Computers and Information Technology (JJCIT). 2020 Sep;6(03):52-71.
[15]. Ortigosa A, Martín JM, Carro RM. Sentiment analysis in Facebook and its application to e-learning. Computers in human behavior. 2014 Feb 1;31:527-41.