SENTIMENT ANALYSIS OF TWEETS ABOUT KARABAKH IN TWITTER BY APPLYING MACHINE LEARNING TECHNIQUES

Aug 5, 2026

Recent years have made social media platforms powerful signals of public sentiment, capturing a wide range of emotions and opinions around major events. This thesis investigates sentiments expressed on Twitter about the Karabakh conflict, treating it as a prolonged and persistent issue. Advanced sentiment analysis techniques are used to study a large corpus of related tweets, classifying them into positive, negative, and neutral categories. Using natural language processing with machine learning models, tweets are vectorized with Count Vectorizer and TF-IDF, and model comparison identifies Logistic Regression as performing well relative to other approaches. The findings inform sentiment analysis, social media analytics, and conflict studies by clarifying how digital discourse reflects and shapes public opinion.

LanguageEnglish

CategoryResearch & Report

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Pages48

Reading Time2 h 1 min

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REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY Institute of Graduate Studies Data Analytics SENTIMENT ANALYSIS OF TWEETS ABOUT KARABAKH IN TWITTER BY APPLYING MACHINE LEARNING TECHNIQUES Sanan QIYASZADE Master’s Thesis Supervisor Asst. Prof. Dr. Oğuz KARAN İstanbul, 2024 SENTIMENT ANALYSIS OF TWEETS ABOUT KARABAKH IN TWITTER BY APPLYING MACHINE LEARNING TECHNIQUES Sanan QIYASZADE Data Analytics Master’s Thesis ALTINBAŞ UNIVERSITY The thesis titled SENTIMENT ANALYSIS OF TWEETS ABOUT KARABAKH IN TWITTER BY APPLYING MACHINE LEARNING TECHNIQUES prepared by SANAN QIYASZADE and submitted on 28/06/2024 has been accepted unanimously for the degree of Master of Science in Data Analytics. Asst. Prof. Oğuz KARAN Supervisor Thesis Defense Committee Members: Asst. Prof. Dr. Oğuz KARAN Department of Software Engineering, Altınbaş University Assoc. Prof. Dr. Sefer KURNAZ Department of Computer Engineering, Altınbaş University Asst. Prof. Dr. Serdar KARGIN Department of Biomedical Engineering, Istanbul Arel University I hereby declare that this thesis meets all format and submission requirements for a Master’s Thesis. I hereby declare that all information/data presented in this graduation project has been obtained in full accordance with academic rules and ethical conduct. I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and vice versa as required by the abovementioned rules and conduct. Sanan QIYASZADE Signature DEDICATION I would like to dedicate my thesis to my family, whose support, motivation, and sacrifices have been the foundation of my academic journey. Their endless love, understanding, and trust in me have been the only source of strength throughout this effort. Also, I am truly thankful to my thesis advisor, Asst. Prof. Oğuz KARAN, whose support, and guidance were very valuable. ABSTRACT SENTIMENT ANALYSIS OF TWEETS ABOUT KARABAKH IN TWITTER BY APPLYING MACHINE LEARNING TECHNIQUES QIYASZADE, Sanan M. Sc., Data Analytics, Altınbaş University, Supervisor: Asst. Prof. Dr. Oğuz KARAN Date: May / 2024 Pages: 43 In recent years, social media platforms have become powerful sources of public sentiment, reflecting the diverse spectrum of emotions and opinions on global events. This thesis delves into the sentiments expressed on Twitter regarding the Karabakh conflict, a longstanding and continuous issue. Through advanced sentiment analysis techniques, this study examinesa major corpus of tweets related to Karabakh, aiming to evaluate the prevailing sentiments, patterns, and trends within the discourse. Our analysis categorizes tweets into positive, negative, or neutral sentiments, relying on state-of-the-art sentiment analysis methodologies. Utilizing natural language processing and machine learning algorithms, we were able to compare machine learning models and acquire the top recommended ML model. More than 10.000 tweets were collected and analysed. In this research Count Vectorizer and TF-IDF vectorizers have been applied to convert textual data into numerical vectors. At the end, we concluded that Logistic Regression performed well compared to other ML models, which will be given in proceeding steps. The implications of this research extend beyond academic inquiry, offering a nuanced understanding of how social media platforms reflect and influence public opinion during protracted geopolitical conflicts. This study contributes to the fields of sentiment analysis, social media analytics, and conflict studies, by providing information about the relationship between digital discourse and real-world conflicts. Keywords: Sentiment Analysis, Machine Learning, Natural Language Processing, Social Media, Public Opinion, Karabakh Conflict. ÖZET TWITTER'DA KARABAĞ HAKKINDA ATILAN TWEETLERİNMAKİNE ÖĞRENMESİ TEKNİKLERİ UYGULANARAK DUYGU ANALİZİ QIYA

FAQs

What is the main goal of the thesis?

The thesis aims to analyze sentiments expressed on Twitter about the Karabakh conflict and evaluate prevailing sentiments, patterns, and trends in the discourse.

How are tweets transformed for machine learning?

Tweets are converted into numerical vectors using Count Vectorizer and TF-IDF vectorization before applying sentiment classification models.

Which machine learning model performed best in the study?

Logistic Regression performed well compared to other machine learning models considered in the research.

Aug 5, 2026

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