Analysis of Text Corpuses From Linguistic Aspects Using Python

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dc.contributor.advisorTóth, Erzsébet
dc.contributor.authorKhalid, Abdullah
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2024-06-23T18:33:24Z
dc.date.available2024-06-23T18:33:24Z
dc.date.created2024-04-16
dc.description.abstractNatural Language Preprocessing (NLP) tasks like sentiment analysis and text classification have gained popularity as techniques to estimate investor sentiment towards a certain stock. This study performs a stock sentiment analysis using textual data from Reddit related to the TSLA stock. It employs a degree of machine and deep learning algorithms to train a classifier model for multi-class classification of the text sentiment, incorporating novel techniques for sentiment reproduction. Additionally, the study investigates the impact of data preprocessing techniques such as undersampling and tokenization on the performance of the sentiment analysis model, providing insights into the effectiveness of these methods in handling imbalanced textual data. The research also explores the influence of machine learning on discerning stock market sentiment, interpreting its ramifications on investor behavior and market dynamics.
dc.description.courseProgramtervező informatikus
dc.description.degreeBSc/BA
dc.format.extent46
dc.identifier.urihttps://hdl.handle.net/2437/374610
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectNatural Language Processing (NLP)
dc.subjectSentiment Analysis
dc.subjectMulti-Class Text Classification
dc.subject.dspaceInformatics::Computer Science
dc.titleAnalysis of Text Corpuses From Linguistic Aspects Using Python
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