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Electronic commerce, commonly known as e-commerce, refers to the buying and selling of goods and services via the internet. This digital evolution allows consumers to shop from the convenience of their homes or offices at any time, enhancing accessibility and flexibility. E-commerce benefits businesses by enabling them to offer round-the-clock service, which often leads to increased transactions and customer satisfaction. Platforms such as review sites, online shopping portals, and blogs empower nearly everyone to share their views on products and services (Kanwal et al., 2024). A 2024 survey by Adobe Digital Economy Index found that 85% of online shoppers now conduct extensive research online before making a purchase decision, highlighting the influential role of the internet in consumer behavior (Kumar et al., 2024).
Moreover, the emotional range expressed in online reviews—from positive to negative sentiments—can profoundly impact consumer choices and corporate reputations. These reviews not only inform potential buyers about the quality of products and services but also play a crucial role in shaping a company’s image and potentially its revenues. Positive reviews can boost consumer trust and attract more customers, while negative feedback can harm a company’s credibility and deter potential buyers (Alslaity et al., 2024). In today’s competitive market, with a plethora of options available, customers highly value the experiences shared by others. Managing and responding to customer feedback is essential for survival in the competitive realm of e-commerce. For instance, if someone is considering buying headphones, they are likely to consult online reviews to gauge the opinions of past customers, which will significantly influence their purchasing decision.
Reviews can generally be classified into two main types: quantitative, such as star ratings, and qualitative, such as lists of pros and cons, as illustrated in Figure 1. These reviews encompass annotations in the form of words or images that convey the reviewer’s sentiments. Quantitative measures, notably star ratings, are commonly used on many online retail sites to gauge consumer opinions. Classification involves organizing different types of reviews or feedback into distinct categories (Singh et al., 2024). Reviews can be emotionally categorized in various ways, including positive, negative, neutral, and these sentiments can be broken down into other specific emotions such as anger, sadness, happiness, and annoyance.
Figure 1. Textual review example with customer ratings
Consequently, consumers increasingly rely on product reviews to gather information and make well-informed purchasing decisions (Mei et al., 2023). However, with the overwhelming amount of available information, approximately 32% of consumers report feeling confused and 30% express frustration. Despite this, many still prefer to read through several reviews before choosing a product. To mitigate these challenges, sentiment analysis (SA) has become an essential tool. It helps extract, evaluate, and present the sentiments of individuals on social media platforms.
SA, a branch of natural language processing, automates the identification of opinions from textual data (Miah et al., 2024). Its primary aim is to classify user-generated reviews as negative or positive based on the expressed sentiment regarding a specific topic. According to Liu ([date]), the terms “opinion mining“ and “SA“ are interchangeable and involve the study of behaviors, attitudes, feelings, emotions, evaluations, categorizations, opinions, and sentiments related to various aspects of services, products, or individuals.