{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:28:58Z","timestamp":1742920138110,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031488573"},{"type":"electronic","value":"9783031488580"}],"license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-48858-0_37","type":"book-chapter","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T06:02:43Z","timestamp":1702965763000},"page":"468-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Domain-Specific Sentiment Analysis of\u00a0Tweets Using Machine Learning Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5197-7802","authenticated-orcid":false,"given":"Tshephisho Joseph","family":"Sefara","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7624-2415","authenticated-orcid":false,"given":"Mapitsi Roseline","family":"Rangata","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"37_CR1","unstructured":"Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.J.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), pp. 30\u201338 (2011)"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Anjaria, M., Guddeti, R.M.R.: Influence factor based opinion mining of Twitter data using supervised learning. In: 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS), pp. 1\u20138. IEEE (2014)","DOI":"10.1109\/COMSNETS.2014.6734907"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"37_CR4","unstructured":"Dhole, K., et al.: NL-Augmenter: a framework for task-sensitive natural language augmentation. Northern Europ. J. Lang. Technol. 9(1) (2023)"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"El Rahman, S.A., AlOtaibi, F.A., AlShehri, W.A.: Sentiment analysis of Twitter data. In: 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/ICCISci.2019.8716464"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Elbagir, S., Yang, J.: Twitter sentiment analysis using natural language toolkit and VADER sentiment. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 122, p. 16 (2019)","DOI":"10.1142\/9789811215094_0005"},{"key":"37_CR7","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/978-3-319-74690-6_51","volume-title":"The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)","author":"MM Fouad","year":"2018","unstructured":"Fouad, M.M., Gharib, T.F., Mashat, A.S.: Efficient Twitter sentiment analysis system with feature selection and classifier ensemble. In: Hassanien, A.E., Tolba, M.F., Elhoseny, M., Mostafa, M. (eds.) AMLTA 2018. AISC, vol. 723, pp. 516\u2013527. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-74690-6_51"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Gautam, G., Yadav, D.: Sentiment analysis of Twitter data using machine learning approaches and semantic analysis. In: 2014 Seventh International Conference on Contemporary Computing (IC3), pp. 437\u2013442. IEEE (2014)","DOI":"10.1109\/IC3.2014.6897213"},{"issue":"16","key":"37_CR9","doi-asserted-by":"publisher","first-page":"6266","DOI":"10.1016\/j.eswa.2013.05.057","volume":"40","author":"M Ghiassi","year":"2013","unstructured":"Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266\u20136282 (2013)","journal-title":"Expert Syst. Appl."},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Hasan, M.R., Maliha, M., Arifuzzaman, M.: Sentiment analysis with NLP on Twitter data. In: 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/IC4ME247184.2019.9036670"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, pp. 216\u2013225 (2014)","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Joyce, B., Deng, J.: Sentiment analysis of tweets for the 2016 US presidential election. In: 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), pp. 1\u20134. IEEE (2017)","DOI":"10.1109\/URTC.2017.8284176"},{"key":"37_CR13","unstructured":"Kaur, P., Edalati, M.: Sentiment analysis on electricity Twitter posts. arXiv preprint arXiv:2206.05042 (2022)"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Mabokela, K.R., Celik, T., Raborife, M.: Multilingual sentiment analysis for under-resourced languages: a systematic review of the landscape. IEEE Access (2022)","DOI":"10.1109\/ACCESS.2022.3224136"},{"key":"37_CR15","unstructured":"Mabokela, R., Schlippe, T.: A sentiment corpus for South African under-resourced languages in a multilingual context. In: Proceedings of the 1st Annual Meeting of the ELRA\/ISCA Special Interest Group on Under-Resourced Languages, pp. 70\u201377 (2022)"},{"key":"37_CR16","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.procs.2021.12.215","volume":"198","author":"H Malik","year":"2022","unstructured":"Malik, H., Shakshuki, E.M., et al.: Approximating viewership of streaming TV programs using social media sentiment analysis. Procedia Comput. Sci. 198, 94\u2013101 (2022)","journal-title":"Procedia Comput. Sci."},{"key":"37_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-030-57321-8_21","volume-title":"Machine Learning and Knowledge Extraction","author":"V Marivate","year":"2020","unstructured":"Marivate, V., Sefara, T.: Improving short text classification through global augmentation methods. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2020. LNCS, vol. 12279, pp. 385\u2013399. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-57321-8_21"},{"issue":"4","key":"37_CR18","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","volume":"5","author":"W Medhat","year":"2014","unstructured":"Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093\u20131113 (2014)","journal-title":"Ain Shams Eng. J."},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Mokgonyane, T.B., Sefara, T.J., Manamela, M.J., Modipa, T.I.: The effects of data size on text-independent automatic speaker identification system. In: 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ICABCD.2019.8851018"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Neethu, M., Rajasree, R.: Sentiment analysis in Twitter using machine learning techniques. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1\u20135. IEEE (2013)","DOI":"10.1109\/ICCCNT.2013.6726818"},{"issue":"2","key":"37_CR21","volume":"1","author":"AS Neogi","year":"2021","unstructured":"Neogi, A.S., Garg, K.A., Mishra, R.K., Dwivedi, Y.K.: Sentiment analysis and classification of Indian farmers\u2019 protest using Twitter data. Int. J. Inform. Manage. Data Insights 1(2), 100019 (2021)","journal-title":"Int. J. Inform. Manage. Data Insights"},{"key":"37_CR22","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"37_CR23","doi-asserted-by":"publisher","unstructured":"Prabhat, A., Khullar, V.: Sentiment classification on big data using naive Bayes and logistic regression. In: 2017 International Conference on Computer Communication and Informatics (ICCCI), pp. 1\u20135 (2017). https:\/\/doi.org\/10.1109\/ICCCI.2017.8117734","DOI":"10.1109\/ICCCI.2017.8117734"},{"key":"37_CR24","doi-asserted-by":"publisher","unstructured":"Ramadhan, W., Astri Novianty, S., Casi Setianingsih, S.: Sentiment analysis using multinomial logistic regression. In: 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pp. 46\u201349 (2017). DOI: https:\/\/doi.org\/10.1109\/ICCEREC.2017.8226700","DOI":"10.1109\/ICCEREC.2017.8226700"},{"key":"37_CR25","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-031-20316-9_15","volume-title":"Advanced Research in Technologies, Information, Innovation and Sustainability","author":"LAP Ramirez","year":"2022","unstructured":"Ramirez, L.A.P., Marquez, B.Y., Magdaleno-Palencia, J.S.: Neuromarketing to discover customer satisfaction. In: Guarda, T., Portela, F., Augusto, M.F. (eds.) Advanced Research in Technologies, Information, Innovation and Sustainability, pp. 191\u2013204. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20316-9_15"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Sefara, T.J., Zwane, S.G., Gama, N., Sibisi, H., Senoamadi, P.N., Marivate, V.: Transformer-based machine translation for low-resourced languages embedded with language identification. In: 2021 Conference on Information Communications Technology and Society (ICTAS), pp. 127\u2013132. IEEE (2021)","DOI":"10.1109\/ICTAS50802.2021.9394996"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Sefara, T.J., Mokgonyane, T.B.: Emotional speaker recognition based on machine and deep learning. In: 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/IMITEC50163.2020.9334138"},{"key":"37_CR28","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1016\/j.procs.2020.03.348","volume":"167","author":"S Vashishtha","year":"2020","unstructured":"Vashishtha, S., Susan, S.: Inferring sentiments from supervised classification of text and speech cues using fuzzy rules. Procedia Comput. Sci. 167, 1370\u20131379 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Vijay, T., Chawla, A., Dhanka, B., Karmakar, P.: Sentiment analysis on covid-19 Twitter data. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1\u20137. IEEE (2020)","DOI":"10.1109\/ICRAIE51050.2020.9358301"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Xia, E., Yue, H., Liu, H.: Tweet sentiment analysis of the 2020 US presidential election. In: Companion Proceedings of the Web Conference 2021, pp. 367\u2013371 (2021)","DOI":"10.1145\/3442442.3452322"}],"container-title":["Communications in Computer and Information Science","Advanced Research in Technologies, Information, Innovation and Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48858-0_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T06:14:32Z","timestamp":1702966472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48858-0_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,20]]},"ISBN":["9783031488573","9783031488580"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48858-0_37","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,12,20]]},"assertion":[{"value":"20 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARTIIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"artiis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/artiis.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}