Occupational groups prediction in Turkish Twitter data by using machine learning algorithms with multinomial approach

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dc.contributor.authorÇıplak, Zeki
dc.contributor.authorYıldız, Kazım
dc.date.accessioned2024-06-13T20:15:53Z
dc.date.available2024-06-13T20:15:53Z
dc.date.issued2024
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Bilgisayar Programcılığı Programı
dc.description.abstractA lot of research has been done on personality and sentiment analysis, demographic and professional aspects using user shares in social networks. In particular, information extraction and value are produced based on Twitter data. This study aims to predict the users, occupational groups, who share in Turkish on Twitter, using machine learning methods. First, occupational groups and the Twitter accounts of the occupations in these occupational groups were determined manually and the tweets shared in these accounts were scraped. All tweets were then grouped by occupation into groups of one, five and ten, creating datasets with different characteristics, each containing more than 500,000 tweets. Some datasets were preprocessed using the Zemberek library, which is used in many Turkish NLP studies, and experiments were conducted out with a total 6 datasets. During the preprocessing phase, since the ready-made stopwords lists were not considered sufficient, unnecessary word lists consisting of single and binary words were created manually. Count and TF-IDF vectorizers are used to convert textual data into numerical. Since each word represents a variable in the text classification study, new variables were created by combining double and triple word phrases (ngrams) with feature extraction. In the experiments in which 24 different models were run, instead of using all the features created, the method of “determining the optimal number of features”, which consists of the most valuable features, was used. It was found that the most successful model in the experiments using machine learning algorithms with a multinomial approach achieved 97.3% success in all calculated metrics.
dc.identifier.doi10.1016/j.eswa.2024.124175
dc.identifier.issn0957-4174
dc.identifier.scopus2-s2.0-85192984274
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2024.124175
dc.identifier.urihttps://hdl.handle.net/11501/924
dc.identifier.volume252
dc.identifier.wosWOS:001240775500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÇıplak, Zeki
dc.institutionauthorid0000-0002-0086-3223
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofExpert Systems with Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectOccupation Prediction
dc.subjectMachine Learning
dc.subjectTurkish Twitter Data Analysis
dc.subjectMultinomial Approach
dc.subjectData Mining
dc.titleOccupational groups prediction in Turkish Twitter data by using machine learning algorithms with multinomial approach
dc.typeArticle

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