MASHINALI O‘RGATISH USULLARI YORDAMIDA MATNNI SENTIMENT TAHLIL QILISH
Keywords:
Text sentiment, sentiment analysis, machine learning, deep learning, artificial neural networks, Naive Bayes, Support Vector Machine, Logistic Regression, natural language processing (NLP).Abstract
Sentiment analysis is one of the crucial tasks in the field of Natural Language Processing (NLP). This method determines the emotional state of the text, whether it is positive, negative, or neutral. This thesis analyzes the methods of sentiment analysis and their approaches based on machine learning and deep learning. First, the theoretical foundations of sentiment analysis and its application using software tools are discussed.
In the thesis, machine learning methods such as Naive Bayes, Support Vector Machine (SVM), and Logistic Regression are described for sentiment classification. Additionally, deep learning methods, particularly artificial neural networks and modern transformer models, are analyzed for their ability to contextualize and recognize complex grammatical structures.
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