Sentiment Analysis towards Car Reviews With Data Visualization

Abstract

Nowadays, there are too many car reviews on the internet, worldwide. Big manufacturing companies use user feedback to improve product quality by understanding the user perspective. Customers will read reviews on websites or on social media platforms before deciding which cars to buy, and may consider testing at a nearby showroom. So, reviews are very important to both the manufacturer and the customer. Nevertheless, it is hard to extract useful information from hundreds or thousands of reviews on websites or social media platforms. Sentiment analysis is applied across various areas, such as business and products, to analyse and learn from people’s opinions. Fine-grained sentiment analysis is best for analysing the polarity of a sentence and determining its sentiment —positive, negative, or neutral. After preprocessing the reviews, extract features and use Naïve Bayes to classify sentiment. The results will be displayed in the dashboard visualisation so the user can read all the reviews properly. Functional testing is conducted to ensure the system runs smoothly, as it should. There is a need to improve this system, as some of these car models are not very common in Malaysia. Later, we can get data on Malaysia's standard car models and apply the system to them. The model's classification method accuracy could be improved by training and testing the system on a large number of reviews.