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In today’s global business environment, e-commerce has become essential, continually advancing and expanding. With a growing preference for online shopping, consumers are increasingly turning to e-commerce to meet their needs, making it a fundamental aspect of contemporary life. Yet, even with its rapid growth, the e-commerce sector faces significant challenges, with one of the foremost being the intricate relationship between consumer trust and purchase intention (Yu et al., 2021). Simultaneously, social media have emerged as key platforms where people share their opinions and emotions. Effectively extracting consumer sentiment and gaining a deeper insight into the decision-making processes behind their purchases have become essential goals. Sentiment analysis, a technique in natural language processing (NLP), takes on an important role in achieving these objectives (Grover, 2022); it is used to extract sentiment information from text to assist companies in understanding consumers’ emotional tendencies and attitudes. Concurrently, consumer purchase intent prediction represents a valuable tool for gaining insight into the consumer decision-making process. The integration of these two approaches can facilitate the acquisition of more comprehensive market insights and the optimization of marketing strategies.
Sentiment analysis, a significant NLP technique, is becoming a crucial tool for comprehending consumer behavior and market dynamics. Sentiment analysis is a technique used to extract sentiment information from text, identify the people’s affective tendencies (e.g., positive, negative, or neutral), and categorize them. Moreover, sentiment analysis can be expanded to incorporate more advanced methods capable of detecting specific emotions, intentions, or nuanced emotional expressions (e.g., anger, happiness, or sarcasm), as well as identifying context-specific sentiments often found in areas like product reviews (Cambria et al., 2017). Sentiment analysis system architecture (Devika et al., 2016), including data collection, data cleansing, text coding, feature extraction, training models, and evaluating models, is shown in Figure 1. Basing their analysis on this overall system architecture, researchers can extract valuable information from textual content to personalize the sentiment expressed in the textual data.
Figure 1. Working process of sentiment analysis
In recent years, deep learning has advanced considerably in both social media and e-commerce fields. It is particularly effective in analyzing consumer behavior, conducting sentiment analysis, and predicting purchase intentions (Alzahrani et al., 2022). All these methods largely rely on the powerful self-learning capabilities of deep learning networks, including feature extraction, contextual modeling, and big data processing. These capabilities facilitate more effective extraction of emotional information from text and enhance the comprehension and prediction of consumer purchase decisions. Meanwhile, these research advancements come with a range of challenges and issues. For instance, the exponential increase in data volume, along with the diversity and complexity arising from multiple data sources, will require more advanced computational resources and greater processing power to support deep learning model. Furthermore, e-commerce product reviews comprise a multitude of emotional and semantic elements, necessitating the development of sophisticated deep learning models to effectively grasp and comprehend their nuances. Additionally, e-commerce is a multidisciplinary field that requires the integration of expertise from various disciplines, providing enterprises with a broader range of solutions.