02697ntm a2200337 i 4500 0 0 lcc 0 0 10000 10000 2025-05-02 0 CD13574 T000003172 2025-05-02 00:00:00 2025-05-02 PSM 102484 102490 MY-KuUP 20251125111037.0 t||||fs|||| 000 0 ta 250502t20232023my a|||fs|||| 000 0 eng d THE0010019 (Local) Hardback UMPSA eng UMPSA rda PSM .R35 2023 r Bc. Nur Ra' Izzati Binti Zahari, author. Sentiment analysis on automotive brand perception in malaysia using twitter data / Nur Ra' Izzati Binti Zahari Kuantan, Pahang : UMPSA, 2023 © 2023 xi, 57 pages : illustrations ; 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Centre for Mathematical Sciences Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 Includes bibliographical references 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. Centre for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Project Dissertations lcc PSM