Sentiment analysis: a tutorial with Python

Purpose: This study explores advanced approaches and algorithms in sentiment analysis, such as machine learning, deep learning, and transformer architectures. The focus is on understanding consumer opinions, emotions, and attitudes, aiding decision-making and strategy development in marketing and e-commerce. Design/Methodology/Approach: A real-world e-commerce dataset from Brazil demonstrated the extraction, processing, and analysis of product reviews. Machine learning algorithms and deep learning techniques were utilized, highlighting their practical applications and limitations. Findings: The results illustrate how these techniques enable a deeper understanding of consumer sentiment and show that these methodologies facilitate strategic decision-making based on sentiment analysis. Originality/value: This study analyzed a combination of theoretical concepts and practical applications, emphasizing the strengths and limitations of each approach. It contributes to the literature by providing a comprehensive overview of sentiment analysis techniques and their practical implementation. The study's originality lies in its detailed practical approach, which demonstrates the application of sentiment analysis techniques on a real dataset from the Brazilian e-commerce sector. Academics and practitioners interested in applying these techniques in real-life settings will find this study valuable.