From valence to emotions: Exploring the distribution of emotions in online product reviews

Ullah, Rahat; Amblee, Naveen; Kim, Wonjoon; Lee, Hyun-Jong

doi:10.1016/j.dss.2015.10.007

Cited by 99 publications

(63 citation statements)

References 52 publications

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“…The findings suggest that positive emotional content have a positive effect and negative emotional content have no effect on perceived helpfulness. More recently, emotional content of product reviews are examined by Ullah, Amblee et al (2016) using NLP techniques. The results reveal that more extreme reviews have a greater proportion of emotional content as compared to less extreme reviews.…”

Section: Consumer Sentiments Emotion Features and Review Helpfulnessmentioning

confidence: 99%

Helpfulness of product reviews as a function of discrete positive and negative emotions

Malik

Hussain

2017

Computers in Human Behavior

141

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The product review plays an important role in customer's purchase decision making process on the e-commerce websites. Emotions can significantly influence the way that reviews are processed. The importance of discrete emotions embedded in online reviews and their impact on review helpfulness is not explored intensively in prior studies. This study builds a helpfulness predictive model using deep neural network and investigates the influences of emotions that contribute to review helpfulness. We present an approach that extract novel discrete positive and negative emotion features from textual content of product reviews using NRC emotion Lexicon. In addition, the type of product, reviewer, visibility, readability, linguistics and sentiment related characteristics are also used for comparison and helpfulness prediction. The experimental results on two real-life datasets demonstrate that positive emotion features are the best predictors when individual category of feature is considered. However, negative emotion features and visibility features have comparable performance. Furthermore, the hybrid set of features with positive emotion features produce the best predictive performance for helpfulness of online reviews. The empirical evaluation finds that Trust, Joy and Anticipation (positive emotions); Anxiety and Sadness (negative emotions) are most influential emotion dimensions and have greater impact on perceived helpfulness. The findings of this study highlight the importance of emotions in online reviews and have significant implications for consumer and ecommerce retailers.

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Section: Consumer Sentiments Emotion Features and Review Helpfulnessmentioning

confidence: 99%

Helpfulness of product reviews as a function of discrete positive and negative emotions

Malik

Hussain

2017

Computers in Human Behavior

141

View full text Add to dashboard Cite

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“…It is evident that affective computing facilitates decision-making in all operational areas of businesses, such as management, marketing, and finance. For instance, firms can infer the perceived emotion of customers from online product reviews and base managerial decisions on this data in order to support product development (Ullah et al, 2016) and advertising (Ang & Low, 2000). In a financial context, emotional media content has been identified as a driver in the decision-making of investors (Pröllochs et al, 2016), which can thus serve as a decision rule for stock investments (Gilbert & Karahalios, 2010).…”

Section: Further Use Cases Of Deep-learning-based Affective Computingmentioning

confidence: 99%

“…Strategy development Identification of perceived emotion towards products as a lever for product development (Ullah et al, 2016) Brand management Emotion analysis of firm-related tweets for reputation management (Al-Hajjar & Syed, 2015) Churn prediction Emotions within customer responses to marketing content serve as a predictor of purchase intention (Ang & Low, 2000) Preference learning Examination of consumer behavior and emotional attitudes related to product preferences (Chitturi et al, 2007) User interaction…”

Section: Management and Marketingmentioning

confidence: 99%

Deep learning for affective computing: Text-based emotion recognition in decision support

Kratzwald

Ilić

Kraus

et al. 2018

Decision Support Systems

289

117

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Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within narrative documents presents a challenging undertaking due to the complexity and ambiguity of language. Performance improvements can be achieved through deep learning; yet, as demonstrated in this paper, the specific nature of this task requires the customization of recurrent neural networks with regard to bidirectional processing, dropout layers as a means of regularization, and weighted loss functions. In addition, we propose sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition. The resulting performance is evaluated in a holistic setting across 6 benchmark datasets, where we find that both recurrent neural networks and transfer learning consistently outperform traditional machine learning. Altogether, the findings have considerable implications for the use of affective computing.

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“…Many researchers are interested in the credibility of online review valence. Most studies find that negative reviews are more important than positive ones in consumers purchase decisions (Ullah, Amblee, Kim, & Lee, 2016;Pietri & Shook, 2013;Lee & Koo, 2012;Lee et al, 2008). Therefore, we assume the information credibility of negative and positive reviews as 0.6 and 0.4, respectively.…”

Section: Parameter Assignmentmentioning

confidence: 99%

The Dynamic Effects of Online Product Reviews on Purchase Decisions

Chen

Kou

Peng

2018

Technological and Economic Development of Economy

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Previous studies have demonstrated that online reviews play an important role in the purchase decision process. Though the effects of positive and negative reviews to consumers’ purchase decisions have been analyzed, they were examined statically and separately. In reality, online review community allows everyone to express and receive opinions and individuals can reexamine their opinions after receiving messages from others. The goal of this paper is to study how potential customers form their opinions dynamically under the effects of both positive and negative reviews using a numerical simulation. The results show that consumers with different membership levels have different information sensitivities to online reviews. Consumers at low and medium membership levels are often persuaded by online reviews, regardless of their initial opinion about a product. On the other hand, online reviews have less effect on consumers at higher membership levels, who often make purchase decisions based on their initial impressions of a product.

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From valence to emotions: Exploring the distribution of emotions in online product reviews

Cited by 99 publications

References 52 publications

Helpfulness of product reviews as a function of discrete positive and negative emotions

Helpfulness of product reviews as a function of discrete positive and negative emotions

Deep learning for affective computing: Text-based emotion recognition in decision support

The Dynamic Effects of Online Product Reviews on Purchase Decisions

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