International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 16, No. 5, 8 Oct. 2024
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A Machine Learning Approach for Sentiment Analysis Using Social Media Posts
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Index Terms
Sentiment Analysis Twitter Datasets Bagging Ensemble Classifier Machine Learning
Abstract
Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.
Cite This Paper
Ritushree Narayan, Pintu Samanta, "A Machine Learning Approach for Sentiment Analysis Using Social Media Posts", International Journal of Information Technology and Computer Science(IJITCS), Vol.16, No.5, pp.23-35, 2024. DOI:10.5815/ijitcs.2024.05.02
Reference
[1]Asiaee T A. Tepper M. Banerjee A. Sapiro G.: 2012If you are happy and you know it... Tweet. In: (2012) Proceedings of the 21st ACM international conference on Information and knowledge management. pp. 1602–1606. ACM.
[2]Bakliwal A. Arora P. Madhappan S. Kapre N. Singh M. Varma V.: 2012Mining sentiments from tweets.Proceedings of the WASSA 12.
[3]Bravo-Marquez F. Mendoza M. Poblete B.: 2012 Combining strengths emotions and polarities for boosting Twitter sentiment analysis. In: 2013 Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining. ACM 2013.
[4]Chalothorn T. Ellman J.:2013Top: Using Twitter to analyze the polarity of contexts. In:In Proceedings of the seventh international workshop on Semantic Evaluation Exercises (SemEval-2013) Atlanta Georgia USA June 2013.
[5]Deitrick W. Hu W.: 2013 Mutually enhancing community detection and sentiment analysis on Twitter networks. Journal of Data Analysis and Information Processing 1 19–29 2013.
[6]Diakopoulos N. Shamma D.: 2010 Characterizing debate performance via aggregated Twitter sentiment. In: 2010 Proceedings of the 28th international conference on Human factors in computing systems. ACM.
[7]Gimpel K. Schneider N. O’Connor B. Das D. Mills D. Eisenstein J. Heilmanet.al. :2010Part-of-speech tagging for Twitter: Annotation features and experiments. Tech. rep. DTIC Document.
[8]Go A. Bhayani R. Huang L.: 2009Twitter sentiment classification using distant supervision.CS224N Project Report Stanford.
[9]Hu X. Tang J. Gao H. Liu H.: Unsupervised sentiment analysis with emotional signals. In: (2013) Proceedings of the 22nd international conference on the World Wide Web. pp. 607–618. International World Wide Web Conferences Steering Committee.
[10]Hu X. Tang L. Tang J. Liu H.: Exploiting social relations for sentiment analysis in microblogging.In: 2013 Proceedings of the sixth ACM international conference on Web search and data mining. pp. 537–546. ACM.
[11]Krippendorff K.: 1980 Content analysis: anintroduction to its methodology.
[12]Liu K.L. Li W.J. Guo M.:2012 Emoticon smoothed language models for Twitter sentiment analysis. In: AAAI.
[13]Makrehchi M. Kamel M.S.: 2008 Automatic extraction of domain-specific stopwords from labeled documents. In: Advances in information retrieval pp. 222–233. Springer (2008).
[14]Martınez-C´amara E. Montejo-R´aez A. Martın-Valdivia M. Urena-L´opez L.: Sinai 2013 Machine learning and emotion of the crowd for sentiment analysis in microblogs (2013).
[15]Mohammad S.M. Kiritchenko S. Zhu X.: Nrc-canada: Building the state-of-theart in sentiment analysis of tweets. In: 2013In Proceedings of the seventh international workshop on Semantic Evaluation Exercises (SemEval-2013) Atlanta Georgia USA June 2013. (2013).
[16]Nakov P. Rosenthal S. Kozareva Z. Stoyanov V. Ritter A. Wilson T.: 2013 Semeval2013 task 2: Sentiment analysis in Twitter. In2013 In Proceedings of the 7th International Workshop on Semantic Evaluation. Association for Computational Linguistics. (2013).
[17]P. Vasilić S. Vujnović N. Popović A. Marjanović and Ž. Đurović2018 "Adaboost algorithm in the frame of predictive maintenance tasks" 2018 23rd International Scientific-Professional Conference on Information Technology (IT) 2018 pp. 1-4 doi: 10.1109/SPIT.2018.8350846.
[18]Rahman S Irfan M Raza M Moyeezullah Ghori K Yaqoob S Awais M. 2020 Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living.International Journal of Environmental Research and Public Health. 2020; 17(3):1082. https://doi.org/10.3390/ijerph17031082.
[19]Z. Chen F. Jiang Y. Cheng X. Gu W. Liu and J. Peng2018"XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud" 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) 2018 pp. 251-256 doi: 10.1109/BigComp.2018.00044.
[20]Phan X.H. Nguyen L.M. Horiguchi S.: 2008 Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th international conference on the World Wide Web. pp. 91–100. ACM (2008).
[21]Remus R.: Asvuniofleipzig: 2013 Sentiment analysis in Twitter using data-driven machine learning techniques (2013).
[22]Saif H. He Y. Alani H.:2011Semantic Smoothing for Twitter Sentiment Analysis. In: Proceeding of the 10th International Semantic Web Conference (ISWC) (2011).
[23]Saif H. He Y. Alani H.: 2012 Alleviating data sparsity for Twitter sentiment analysis. (2012) In: Proceedings 2nd Workshop on Making Sense of Microposts (#MSM2012) in conjunction with WWW 2012. Layon France (2012).
[24]Saif H. He Y. Alani H.: 2012 Semantic sentiment analysis of Twitter. In: (2012) Proceedings of the 11th international conference on The Semantic Web. Boston MA (2012)
[25]Shamma D. Kennedy L. Churchill E.:2009Tweet the debates: understanding community annotation of uncollected sources. In: Proceedings of the first SIGMM workshop on Social media. pp. 3–10. ACM (2009)
[26]Speriosu M. Sudan N. Upadhyay S. Baldridge J.: 2011Twitter polarity classification with label propagation over lexical links and the follower graph.In: (2011) Proceedings of the EMNLP First workshop on Unsupervised Learning in NLP. Edinburgh Scotland (2011)
[27]Thelwall M. Buckley K. Paltoglou G.: 2012Sentiment strength detection for the social web.Journal of the American Society for Information Science and Technology 63(1) 163–173 (2012)
[28]Thelwall M. Buckley K. Paltoglou G. Cai D. Kappas A.: 2010 Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61(12) 2544–2558 (2010).
[29]Ritushree Narayan, Kesav Sinha “An Analysis and Detection of Misleading Information on Social Media Using Machine Learning Technique (051121-031256)” Published by IGI global https://www.igi-global.com/chapter/an-analysis-and-detection-of-misleading-information-on-social-media-using-machine-learning-techniques/299200