Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 6Citation - Scopus: 5Enhancing Sentiment Analysis in Stock Market Tweets Through Bert-Based Knowledge Transfer(Springer, 2025-02-26) Cicekyurt, Emre; Bakal, GokhanOne of the widely studied text classification efforts is sentiment analysis. It is a specific examination involving natural language processing and machine learning methods to understand semantic orientation from textual data. Working social media posts, such as tweets, for sentiment analysis, is quite common among researchers due to the speed of information dissemination. In this regard, forecasting stock market tweets is a widely studied research topic. Some studies have revealed a strong connection between sentiment and stock market performance, while others have not found any notable associations. The proposed work shows two distinct approaches to sentiment analysis over the stock market tweets. The first approach employs traditional machine learning algorithms, including logistic regression, random forest, and XGBoost. The second approach constructs deep learning (as a subfield of machine learning) models using LSTM and CNN algorithms to classify the test instances into positive, negative, or neutral classes through ten randomly shuffled data splits. In this study, the labeled data size is gradually increased utilizing a pre-trained model, FinBERT. It is exclusively employed to label unlabeled data instances to integrate them into the experiments. The goal is to monitor the effect of the additional newly-labeled examples on the sentiment analysis performance. The experiments showed that the average F1-score improved by 20% for the deep learning models and 17% for the machine learning models. In the end, the paper reveals a strong positive correlation between training data size and the classification performance of the experimental approaches.Article Citation - WoS: 16Citation - Scopus: 11An Empirical Study of Sentiment Analysis Utilizing Machine Learning and Deep Learning Algorithms(Springernature, 2023-12-09) Erkantarci, Betul; Bakal, GokhanAmong text-mining studies, one of the most studied topics is the text classification task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classification, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifically, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis efforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73-76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.