Nur Ra' Izzati Binti Zahari creator author. text my 2023 2023 monographic eng

electronic

xi, 57 pages : illustrations ; 1 CD-ROM In today's digital era, social media platforms have become a treasure trove of usergenerated content, and automotive companies are increasingly harnessing this data to gain a competitive edge. The ability to deeply understand and measure customer sentiments and actions is crucial, as it directly impacts customer relationships and overall company value. However, as statements become more complex, distinguishing between positive, negative, and neutral sentiments becomes challenging, making traditional sentiment analysis less reliable. To address this issue, this study focuses on developing a robust model for analyzing customer feedback on automobile brands in Malaysia, specifically using sentiment analysis techniques such as Naïve Bayes and Support Vector Machine (SVM). The dataset for this analysis consists of tweets obtained from Twitter, providing a rich source of consumer opinions. By effectively evaluating and interpreting these opinions from social media, automotive companies can enhance their business targets and goals. The impact of this research extends beyond individual companies to benefit the entire automobile sector. A better understanding of customer preferences and perceptions enables companies to refine their business plans and identify areas for improvement. Moreover, this study facilitates a deeper understanding of the industry dynamics and customer behavior, ultimately contributing to increased team output and overall industry growth. specialized Nur Ra' Izzati Binti Zahari Centre for Mathematical Sciences Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 Includes bibliographical references Centre for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Project Dissertations THE0010019 (Local) UMPSA 250502 20251125111037.0 eng