Sentiment Analysis of Flagship Smartphones on Social Media Using Python TextBlob And Naive Bayes Algorithm

Abstract

Social media plays a crucial role in the advancement of organizations, industries, and businesses nowadays. Almost everyone is connected to social media. Each individual can interact and exchange knowledge due to the fusion of technology and social relationships. Sentiment analysis is a technique that allows extracting information from users expressing emotions, perspectives, and opinions on the internet. One strategic sector for implementing sentiment analysis is the technology sector, especially the smartphone industry. The wide range of smartphone variants available today poses a problem for individuals in finding the best smartphone product. The sentiment analysis of flagship smartphones conducted in this article aims to find the best solution between two flagship smartphones from renowned manufacturers, namely the Samsung S22 Ultra and the Xiaomi 12 Pro. The data is collected from various social media platforms such as Twitter, YouTube, and GSMArena. The collected data is then analyzed using Python TextBlob, and the analysis results in negative, positive, and neutral sentiments displayed through various visualizations. The final outcome is the assessment of Net Brand Reputation, which evaluates the reputation of a brand across multiple social media platforms.