Modeling the impact of review dynamics on utility value of a product

Kannan, Kalapriya; Goyal, Manik; Jacob, George T.

doi:10.1007/s13278-012-0086-4

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…As Kannan et al (2012) argue, due to the ever-growing amount of different products with similar characteristics, customers are often searching for authentic, user-generated reviews to estimate the actual utility value of products. Moreover, due to the ubiquity and invasiveness of online advertisement, consumers rely on user-generated opinions as a source of information about products and services (Chevalier and Mayzlin 2006).…”

Section: Introductionmentioning

confidence: 99%

Good location, terrible food: detecting feature sentiment in user-generated reviews

Cataldi

Ballatore

Tiddi

et al. 2013

Soc. Netw. Anal. Min.

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A growing corpus of online informal reviews is generated every day by non-experts, on social networks and blogs, about an unlimited range of products and services. Users do not only express holistic opinions, but often focus on specific features of their interest. The automatic understanding of "what people think" at the feature level can greatly support decision making, both for consumers and producers. In this paper, we present an approach to feature-level sentiment detection that integrates natural language processing with statistical techniques, in order to extract users' opinions about specific features of products and services from usergenerated reviews. First, we extract domain features, and each review is modelled as a lexical dependency graph. Second, for each review, we estimate the polarity relative to the features by leveraging the syntactic dependencies between the terms. The approach is evaluated against a ground truth consisting of set of usergenerated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.

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Section: Introductionmentioning

confidence: 99%

Good location, terrible food: detecting feature sentiment in user-generated reviews

Cataldi

Ballatore

Tiddi

et al. 2013

Soc. Netw. Anal. Min.

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Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation

Brenga

Celotto

Loia

et al. 2016

Sentiment Analysis and Ontology Engineering

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Distilling sentiments and moods hidden in the written (natural) language is a challenging issue which attracts research and commercial interests, aimed at studying the users behavior on the Web and evaluating the public attitudes towards brands, social events, political actions. The understanding of the written language is a very complicated task: sentiments and opinions are concealed in the sentences, typically associated to adjectives and verbs; then the intrinsic meaning of some textual expressions is not amenable to rigid linguistic patterns. This work presents a framework for detecting sentiment and emotion from text. It exploits an affective model known as Hourglass of Emotions, a variant of Plutchik’s wheel of emotions. The model defines four affective dimensions, each one with some activation levels, called ‘sentic levels’ that represent an emotional state of mind and can be more or less intense, depending on where they are placed in the corresponding dimension. Our approach draws from the Computational Intelligence area to provide a conceptual setting to sentiment and emotion detection and processing. The novelty is the fuzzy linguistic modeling of the Hourglass of Emotions: dimensions are modeled as fuzzy linguistic variables, whose linguistic terms are the sentic levels (emotions). This linguistic modeling naturally enables the use of fuzzy linguistic aggregation operators (from Computing with Words paradigm), such as LOWA (Linguistic Ordered Weighted Averaging) that inherently accomplishes an aggregation of the emotions in order to get an emotional expression that synthesizes a set of emotions associated with different sentic levels and activation intensities. The whole process for the emotion detection and synthesis is described through its main tasks, from the text parsing up to emotions extraction, returning a predominant emotion, associated with each dimension of the Hourglass of Emotions. An ad-hoc ontology has been designed to integrate lexical information and relations, along with the Hourglass model

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Data-informed inverse design by product usage information: a review, framework and outlook

Hou

Jiao

2019

J Intell Manuf

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Modeling the impact of review dynamics on utility value of a product

Cited by 6 publications

References 20 publications

Good location, terrible food: detecting feature sentiment in user-generated reviews

Good location, terrible food: detecting feature sentiment in user-generated reviews

Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation

Data-informed inverse design by product usage information: a review, framework and outlook

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