Unveiling Temporal Dynamics of Consumer Emotions in Online Reviews: An Emotion Mining Approach Using Ekman’s Emotion Model

Currently, business organizations are using Electronic Word-of-Mouth (EWOM) from consumer-opinion platforms to elevate their marketing strategies using semantic analysis and emotion mining. Existing efforts are devoted to analyzing EWOM using fine-grained emotion classification; however, the well-known and well-established emotion models that provide a basic set of psychological and biological emotions are not targeted in these studies. Further, the existing literature is unable to present temporal dynamism and volatility of consumers’ emotions according to emotion categories that have an inescapable impact on consumers and corporate decision-making process. Therefore, this study tries to address this gap by extracting, classifying, summarizing, and tracking consumers’ emotions for corporate and consumers’ decision-making processes based on widely used and well-established Ekman's emotions model. Therefore, the current work aims to provide consumers’ emotions temporal dynamism and volatility at product and feature levels by using binary and fine-grained emotion classifications based on Ekman’s basic psychological and biological emotions. Online reviews from amazon.com are used for experimental purposes. The results of the study exhibited that consumers predominantly expressed joy emotion to the camera. However, the picture feature of the camera evoked negative emotions, namely, fear, anger, and sadness. Temporal analysis unveiled shifting emotional expressions in quarter 1 and quarter 2. The results of the study will assist corporates in shaping their decision-making process and marketing strategies. Moreover, the findings of the study provide valuable insights into consumers to improve their purchase decisions by pinpointing the advantages and disadvantages of critical product features through emotional clues.