Discovery and Classification of the Underlying Emotions in the User Generated Content (UGC)

Serna, Ainhoa; Gerrikagoitia, Jon Kepa; Bernabé, Unai

doi:10.1007/978-3-319-28231-2_17

Cited by 18 publications

(14 citation statements)

References 36 publications

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“…Therefore, it can go deeper into the meanings of the words and understand more profoundly what is being said. Just to cite some recent examples, semantic analysis have been employed to assess how the brands of the destinations of Brazil and Mexico are communicated through Facebook [ 74 ], to define a “semantic network” to analyse the relations between topics [ 75 ], to measure the image of the Basque Country, taking cognitive, affective and conative aspects into account [ 76 ] and to study the emotional qualities in tourists’ UGC through the analysis of their perceptions and experiences [ 77 ]. Various authors have combined thematic and semantic analysis in their studies [ 78 – 80 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic analysis and the evolution towards participative branding: Do locals communicate the same destination brand values as DMOs?

2018

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Participative branding is a process by which DMOs, locals and visitors contribute, through their activity in social media, to the definition of the emotional values associated to a destination. From the communicative point of view it is particularly important that DMOs and local citizens work together in the transmission of a set of coherent values that compose a personalised identity. This paper presents a novel methodology, based on semantic similarity measures, that permits to make an automated analysis of the emotional values transmitted by official tourist offices and by local citizens through social media. A study of 54,000 tweets from 9 main European tourist destinations highlights the lack of a strategy towards the communication of a distinctive brand and a strong gap between the official view transmitted by the DMOs of the destinations and the one communicated by their residents.

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Section: Literature Reviewmentioning

confidence: 99%

Semantic analysis and the evolution towards participative branding: Do locals communicate the same destination brand values as DMOs?

2018

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“…A lexicon-based approach has also been used in tourism-related research. For example, Serna, Gerrikagoitia, and Bernabé (2016) used the WordNet (Miller 1995) lexical database to extract emotions from tweets referring to two holiday periods, Easter and summer. Neidhardt, Rümmele, and Werthner (2016) used SentiWordNet (Baccianella, Esuli, and Sebastiani 2010; Esuli and Sebastiani 2006), an opinion-mining extension of WordNet, to mine emotions expressed in an online travel forum.…”

Section: Introductionmentioning

confidence: 99%

Automated Sentiment Analysis in Tourism: Comparison of Approaches

Kirilenko

Степченкова

Kim

et al. 2017

Journal of Travel Research

159

108

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Interest in applying Big Data to tourism is increasing, and automated sentiment analysis has been used to extract public opinion from various sources. This article evaluates the suitability of different types of automated classifiers for applications typical in tourism, hospitality, and marketing studies by comparing their performance to that of human raters. While the commonly used performance indices suggest that on easier-to-classify data sets machine learning methods demonstrate performance comparable to that by human raters, other performance measures such as Cohen’s kappa show that the results of machine learning are still inferior to manual processing. On more difficult and noisy data sets, automated analysis has poorer performance than human raters. The article discusses issues pertinent to selection of appropriate sentiment analysis software and offers a word of caution against using automated classifiers uncritically.

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“…Seemingly, the negative and positive distribution of emotions is more balanced in Plutchik's research. These two approaches are also reflected in UGC research (Serna, et al, 2016).…”

Section: Emotions In Ugcmentioning

confidence: 98%

Comparative Content Analysis of Hotel Reviews by Mass Tourism Destination

Atabay

Çızel

2020

JoTS

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This article examines user-generated content (UGC) related to hotels in three different mass tourism destinations (Antalya, Majorca, and Sharm El Sheikh) that offer services with the all-inclusive system (AIS) to comparatively analyses tourists' evaluations and emotions about service components. While the study was designed with the content analysis method, text mining and sentiment analysis were used together. Customer reviews (UGC) of top hotels in three different mass tourism destinations were collected from an on-line travel review site. A total of 3588 English hotel reviews were analysed by the R program. Analysis of the reviews for famous mass tourism destination hotels in the Mediterranean region has also clearly revealed the priority service characteristics (rooms, staff, and food) and dominant emotions for hotels in all destinations in comparison. Moreover, the multiple correspondence analysis results clearly show how the emotions about the services of the hotels in three different regions diverge. Analysis results provide important clues for mass tourism destination hotels working with AIS.

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Discovery and Classification of the Underlying Emotions in the User Generated Content (UGC)

Cited by 18 publications

References 36 publications

Semantic analysis and the evolution towards participative branding: Do locals communicate the same destination brand values as DMOs?

Semantic analysis and the evolution towards participative branding: Do locals communicate the same destination brand values as DMOs?

Automated Sentiment Analysis in Tourism: Comparison of Approaches

Comparative Content Analysis of Hotel Reviews by Mass Tourism Destination

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