Leveraging Big Data Analytics to Assess the Influence of Online Consumer Sentiment on Chery Omoda 5 Sales in Malaysia

Abstract

With the rapid development of big-data technologies, the influence of online consumer sentiment on purchasing decisions has become increasingly significant, particularly within the automotive industry. This study systematically investigates the impact of negative online opinions on the sales of the Chery Omoda 5 in the Malaysian market by leveraging advanced data-mining, sentiment-analysis, and market-trend analysis techniques. Sales records and large-scale online content—including social-media posts, news articles, forums, and blogs—were collected from January 2024 to June 2024. Natural-language processing (VADER) was applied to quantify negative sentiment, and multiple linear-regression models—controlling for seasonality (month fixed effects) and macroeconomic indicators (fuel price index, consumer-price index)—were estimated to examine the relationship between sentiment fluctuations and monthly sales trends. The results demonstrate a strong negative correlation (r = −0.924) between the frequency of negative online mentions and Omoda 5 sales; peaks in negative sentiment, often triggered by product recalls or intensified media coverage, were temporally aligned with sharp declines in sales volume. These findings provide empirical evidence of the immediate and measurable adverse effects of online negativity on consumer purchasing behavior. The study concludes with recommendations for automotive manufacturers, urging marketing managers to integrate real-time social-listening dashboards into sales-forecasting workflows so that emerging reputation risks can be identified and addressed before they erode market performance, thereby underscoring the value of proactive crisis-communication strategies for protecting brand equity in an increasingly digital marketplace.