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• W3136803084 abstract "Social media platforms are places where people post their feelings and thoughts about a topic. The institutions, organizations, individuals, or companies that are the subject of these ideas are affected by these posts. As discussed in different studies, companies in stock exchange markets are affected by the posts made on these social media platforms. At the same time, individuals who are aware of this fact, namely speculators and influencers, may make profit by manipulating the truth. In this study, possible speculators or influencers using the Twitter social media platform are investigated. As the target companies, Google, Amazon, Apple, Tesla, and Microsoft were chosen, which are among the largest companies on the NASDAQ stock exchange market. In the study, asentiment analysis using the Loughran and McDonald sentiment analysis dictionary was utilized. The sentiment analysis results were used to model different machine learning algorithms. With the models, individuals who had too many positive or negative effects as possible speculators or influencers were identified. The study was performed for 5 years of data. The results indicates that (1) without noise reduction, it is not possible to establish a correlation on individual tweets and their effects on the stock market; (2) it is not possible to establish a correlation between the number of tweets and the volume of companies; (3) the effect of threshold on the accuracy, which has been done and proven in different studies, has also been proven in this study; (4) RBF Kernel SVM method gives better result than other machine learning methods." @default.
• W3136803084 created "2021-03-29" @default.
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• W3136803084 date "2020-12-10" @default.
• W3136803084 modified "2023-10-14" @default.
• W3136803084 title "Speculator and Influencer Evaluation in Stock Market by Using Social Media" @default.
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• W3136803084 doi "https://doi.org/10.1109/bigdata50022.2020.9378170" @default.
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