In the Eye of the Reviewer: An Application of Unsupervised Clustering to User Generated Imagery in Online Reviews

Overgoor, Gijs; Mestri, Rohan; Rand, William

doi:10.24251/hicss.2021.515

Cited by 1 publication

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surge of unstructured data sources has made methods for translating textual and visual content into useful information increasingly necessary. Online reviews have shown to be an incredibly rich source of unstructured information about consumer perceptions and experiences that provide a peek into the minds of its producers and receivers [3,4,5,6,7]. In this research, we sought to improve our understanding of the different modalities that constitute an online review -text and imagery.…”

Section: Discussionmentioning

confidence: 99%

“…In order to extract the most talked about topics from images, we use an Image-based clustering approach. Image-based Clustering to derive a cluster distribution is heavily discussed in [5] which relies on a combination of Transfer Learning and Deep Embedded Clustering [16] and we will modify their setup to reflect hotel-specific findings.…”

Section: Methodsmentioning

confidence: 99%

“…Once we have the cluster distributions, we can calculate the the mean rating of a cluster by computing the mean of all ratings of all datapoints that belong in this particular cluster [5].…”

Section: Image Based Reviewsmentioning

confidence: 99%

“…It allows users to share their experiences with comments, reviews, pictures, ratings on a hotel, destination, or any tourist attraction. These reviews have been shown to be a valuable source of information [3,4,5,6,7] Future consumers resolve uncertainty about the quality of a hotel and its facilities by reading the online reviews given by customers on a website [1]. In addition, expectations of customers usually do not conform with their experience of a hotel because of varied reasons [2].…”

Section: Introductionmentioning

confidence: 99%

“…The UGI added to reviews can also be analyzed to mine consumer opinions and perceptions. The UGI, similar to their textual counterparts, hold valuable information about customer experience and hotel performance along different quality dimensions [14,5].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Text vs. Image: An application of unsupervised multi-modal machine learning to online reviews

Mestri¹,

Bollam²,

Overgoor³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

Online user-generated reviews provide a unique view into consumer perceptions of a business. Extant research has demonstrated that text mining provides insight from textual reviews. More recently, we haven seen the adoption of image mining techniques to analyze visual content as well. With data comprising of user-generated imagery (UGI) and textual reviews, we propose to perform a combination of text-and image mining techniques to extract relevant attributes from both modalities. The analysis allows for a comparison between textual and visual content in online reviews. For the UGI analysis, we use a Deep Embedded Clustering model and for the User Generated Text Analysis we use a TF-IDF based mechanism to obtain attributes and polarities.The overall goal is to extract maximum information from text and images and compare the insights we gather from both. We analyze if any modality is self-sufficient or better than the other and also if both modalities combine to give similar or contrasting insights.

show abstract