Sentiment Analysis in E-Commerce Decision Support
Summary
Sentiment analysis in e-commerce decision support harnesses computational techniques to interpret consumer opinions expressed in online reviews and ratings, transforming qualitative feedback into quantitative insights. By leveraging lexicon-based methods, machine learning and deep learning models, platforms can extract sentiment orientation and intensity across product features to guide purchasers and retailers in selection, pricing and inventory decisions. Integrating such analyses with multi-criteria decision-making frameworks enables the accommodation of dynamic information preferences, risk attitudes and cross-platform review aggregation. Advances in fuzzy set theory, distribution linguistics and compromise programming have yielded robust decision support systems tailored to industries ranging from hospitality to electronics. These innovations enhance market transparency, improve customer satisfaction and inform strategic choices at a global scale, illustrating the growing interconnection between sentiment-analysis methodologies and decision support in e-commerce.
Research from Nature Portfolio
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Research from all publishers
Recent contributions have emphasised the integration of sentiment analysis with advanced multi-criteria decision-making approaches. A 2022 model combines lexicon-based sentiment scoring with an intuitionistic fuzzy TODIM method, translating sentiment orientation and intensity into fuzzy values to rank alternative products, as demonstrated in mobile phone selection. Earlier foundational work applied distribution linguistic evaluations with a DL-VIKOR method to hotel selection, establishing objective and subjective weighting of features before generating compromise rankings. Other investigations have introduced discrete dynamic fuzzy weighted averaging (DDIFWA) operators to account for temporal information preferences in online reviews, converting sentiment orientation classifications into semantic intuitionistic fuzzy numbers and aggregating them into purchase-decision rankings. Together, these studies underscore the efficacy of hybrid fuzzy-MCDM frameworks in capturing nuanced consumer preferences and delivering actionable recommendations for e-commerce stakeholders.
Sentiment Analysis in E-Commerce Decision Support publication trend
The graph below shows the total number of articles in sentiment analysis in e-commerce decision support across all publications each year (not limited to Nature Index journals).
Technical terms
Sentiment analysis: Computational evaluation of textual emotional tone to determine positive, negative or neutral attitudes.
Multi-criteria decision-making (MCDM): Framework for ranking or selecting alternatives based on multiple evaluation criteria.
Intuitionistic fuzzy set: Extension of fuzzy sets capturing degrees of membership, non-membership and hesitation.
VIKOR method: MCDM technique focusing on compromise solutions based on closeness to ideal and nadir alternatives.
Discrete dynamic fuzzy weighted averaging (DDIFWA) operator: Aggregation operator accounting for time-varying sentiment information with fuzzy semantics.
References
- Product selection based on sentiment analysis of online reviews: an intuitionistic fuzzy TODIM method. Complex & Intelligent Systems (2022).
- Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method. Technological and Economic Development of Economy (2019).
- A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators. IEEE Access (2019).
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