Sentiment Analysis with Machine Learning Methods on Social Media

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dc.authoridBASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169
dc.authorwosidBASARSLAN, MUHAMMET SINAN/W-2030-2018
dc.contributor.authorBasarslan, Muhammet Sinan
dc.contributor.authorKayaalp, Fatih
dc.date.accessioned2021-12-01T18:48:00Z
dc.date.available2021-12-01T18:48:00Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractSocial media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naive Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.en_US
dc.identifier.doi10.14201/ADCAIJ202093515
dc.identifier.endpage15en_US
dc.identifier.issn2255-2863
dc.identifier.issue3en_US
dc.identifier.startpage5en_US
dc.identifier.urihttps://doi.org/10.14201/ADCAIJ202093515
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10433
dc.identifier.volume9en_US
dc.identifier.wosWOS:000614558800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherEdiciones Univ Salamancaen_US
dc.relation.ispartofAdcaij-Advances In Distributed Computing And Artificial Intelligence Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSentiment analysisen_US
dc.subjectSocial Mediaen_US
dc.subjectPythonen_US
dc.subjectNatural Language Processingen_US
dc.titleSentiment Analysis with Machine Learning Methods on Social Mediaen_US
dc.typeArticleen_US

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