Abstract:
The rapid growth of the online market, particularly in the digital realm, has spurred the need for in-depth studies regarding
marketing strategies through public opinion, especially on platforms like Twitter. The sentiments expressed in customer tweets hold
significant insights into their satisfaction or dissatisfaction levels with a service. Therefore, the use of ML algorithms in sentiment
analysis is imperative to detect whether such comments lean towards positivity or negativity regarding a service. This research focuses
on sentiment analysis towards three major e-commerce platforms in Indonesia: Tokopedia, Shopee, and Lazada, through the utilization
of Twitter. The classification process involves various stages, including preprocessing, feature extraction and selection, data splitting for
classification, and evaluation. The selection of both linear and non-linear SVM models as the focus of this research is based on their
ability to handle large and complex datasets. The linear kernel is chosen for its proficiency in cases with a linear relationship between
features and class labels, while the non-linear SVM provides flexibility in dealing with complex and non-linear relationships. Based on
the evaluation results of the SVM model on the dataset, it is found that the polynomial kernel provides the highest accuracy value of
93%, with a training data share of 85%. This model features strong prediction capabilities with a precision of 93% for negative and
93% for positive labels. Although the linear kernel and other kernels showed solid performance, the polynomial kernel provided the
most optimal results in the context of online marketplace sentiment analysis using data from Twitter.
