Analysis on Customer Satisfaction Dimensions in Peer-to-Peer Accommodation using Latent Dirichlet Allocation: A Case Study of Airbnb

Situmorang, Kevin; Hidayanto, Achmad Nizar; Yuliawati, Arlisa

doi:10.1109/eecsi.2018.8752912

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Covering multiple areas including review bias, hospitality exchanges, price and neighborhood prediction, neighborhoods ranking, socio-economic characteristics, listing recommendation system, rentals' distribution, users' preferences and expectations, image mining, demand mining, grading schema, matching schema, consumer segmentation, trust evaluation, innovation adoption, and adoption evaluation. In addition to some researches that focus on analyzing customers' satisfaction dimensions [30], analyzing customers feedback using text mining [3], and analyzing public opinions using content analysis [21]. Previous studies have almost exclusively focused on analyzing user reviews separately without linking them to listings data.…”

Section: Airbnbmentioning

confidence: 99%

What Drives Airbnb Customers’ Satisfaction in Amsterdam? A Sentiment Analysis

Madhi¹,

Alhammad²

2021

IJACSA

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The sharing economy is a new socio-economic system that allows individuals to rent out their personal belongings, such as their private car or a room in their home, for a short period. This study aims to investigate the attributes that impact customers' satisfaction when using the sharing economy propriety rentals websites. Large data sets of Airbnb's online reviews and listings in Amsterdam were analyzed using sentiment analysis, word clustering, ordinal logistic regression, and visualization techniques. Findings reveal that the polarity of Airbnb guests reviews in Amsterdam is significantly impacted by property price, value, cleanliness, rate, host communication, easiness of check-in, the accuracy of property description, and whether the host is super host or not. Surprisingly, the property neighborhood was not found to impact customers' sentiment in Amsterdam. In addition, Airbnb guests in Amsterdam tend to positively express their experience satisfaction level mainly based on property exact location and host interaction followed with the facilities surrounding the property, property cleanliness, and room quality. On the other hand, negative online reviews tend to be mainly linked to problems with check-in services followed by aspects related to weak host interaction, location, and room quality. The results indicate that Airbnb hosts need to offer clear and easy check-in services with emphasizing the importance of keeping a good communication channel with their guests to enhance customers' experience and increase customers' satisfaction level. Future studies should investigate the applicability of the findings of this study in the context of other cities.

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Section: Airbnbmentioning

confidence: 99%

What Drives Airbnb Customers’ Satisfaction in Amsterdam? A Sentiment Analysis

Madhi¹,

Alhammad²

2021

IJACSA

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“…review in this study) as a mixture of latent topics, each of which is characterized by a distribution over words (Blei et al , 2003). Prior studies have shown that LDA can discover various attributes from Airbnb reviews (Situmorang et al , 2018; Zhang 2019b). According to Zhang (2019a), LDA is particularly suitable for examining Airbnb reviews for two reasons.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In another study, Zhang (2019b) compared Airbnb topics with the hotel topics identified by Guo et al (2017) and identified unique topics on Airbnb. Situmorang et al (2018) and Ju et al (2019) extracted topics from Airbnb reviews and examined the effect of topics on consumer satisfaction based on sentiment analysis. While most studies conducting sentiment analysis confirmed that Airbnb reviews have positivity bias towards the hosts, Cheng and Jin (2019) identified that topics associated with negative sentiments were noise, floor, shower, parking, and door.…”

Section: Introductionmentioning

confidence: 99%

Improving peer-to-peer accommodation service based on text analytics

Lee

Tse

2020

IMDS

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PurposeThis paper aims to identify key service attributes in peer-to-peer (P2P) accommodation from online reviews and formulate service improvement strategies based on the unsatisfactory service encounters mined from the reviews.Design/methodology/approachThe methodology involves topic modelling using latent Dirichlet allocation, sentiment analysis and process analysis based on process chain network (PCN).FindingsThe text analytics results showed that negative P2P accommodation experiences are caused by the lack of hot water for shower, poor sleep quality and unpleasant check-in.Research limitations/implicationsThe PCN analysis shows that the surrogate interactions of the P2P accommodation platform with both the guest and the host impact consumer experiences. This highlights that the key to managing consumer experiences lies in the non-human resources such as information, rather than direct interactions between process entities.Practical implicationsThe information on the P2P accommodation platform should be in a more interactive format such as video and 360 degrees camera. Hosts should ensure a good condition of the physical products such as water heaters and beds before guests' arrival. Professional videography and handyperson services should be provided by the platform to help hosts deliver a preferred consumer experience. Flexible and strict check-in polices should also be introduced to smoothen the check-in process.Originality/valueThis study is built on multi-attribute utility theory. It is also one of the first to study P2P accommodation services from an operations management perspective. It demonstrates how text analytics serves as an additional supplement for service improvement.

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“…Existem inúmeras possibilidades de delimitar os parâmetros para a escolha do modelo de tópicos para a pesquisa. Partindo do pressuposto de que cada comentário pode conter tópicos com uma determinada proporção, a LDA localiza os tokens e os agrupa em tópicos (SITUMORANG et al, 2018;ZHANG, 2019a), fornecendo o conteúdo na forma de arquivo de texto do tipo .TXT e em HTML 9 para a visualização por meio do pacote pyLDAvis da biblioteca Gensim. chunksize (tamanho do pedaço) com 300, 500, 600, 810, 900, 1.000, 10.000, 25.000, 40.000 e 48.021 documentos; e contagens mínimas de palavras diferentes para processamento das informações com mínimo de 50, 100 e 200 termos, conforme exemplificado na Tabela 4.…”

Section: Análise De Tópicos Por Ldaunclassified

“…O surgimento e desenvolvimento da tecnologia, facilitada pelo Conteúdo Gerado pelo Usuário (UCG), por meio de recursos para mineração de textos de Big Data, permitiu que fosse realizada a análise de 48.020 comentários de uma única vez, possibilitando a interpretação dos fatores que influenciam a satisfação do consumidor e, consequentemente, dos hóspedes da plataforma (SITUMORANG et al, 2018), dentro do estado da arte, que ainda não havia sido explorada anteriormente na literatura do Turismo e da Hospitalidade.…”

Section: Conclusãounclassified

A influência da qualidade do serviço na hospitalidade domiciliar: um estudo do uso da Airbnb por turistas

Cagnacci

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Aqui eu vou escrever com o coração sobre todos que, de algum modo, fizeram parte dessa trajetória. E são muitos os agradecimentos a fazer, o que me revela ser mais um momento desafiador por não saber por onde começar, pois nada seria possível sem o apoio dessas pessoas aqui citadas. Então, o melhor a fazer é usar do pleonasmo e começar do começo. O primeiro agradecimento vai para o meu orientador acadêmico, professor doutor George Bedinelli Rossi, que me escolheu, acolheu e aceitou seguir comigo nessa jornada, mostrando-se paciente, quando repetidas vezes me alertava como escrever e como pensar academicamente, e sobre como dar um rumo científico ao meu projeto. O professor George, altamente gabaritado e dotado de grande capacidade e inteligência, nunca deixou de me atender e sempre pronta e rapidamente, com incansável disponibilidade, me provendo segurança, ao compartilhar comigo seu conhecimento e experiência, me aconselhando, corrigindo e aprimorando meus trabalhos. Na sequência, vem os professores do programa PPGTUR. Todas as aulas, foram significativas e me ajudaram a dar corpo ao projeto. Entre os professores, destaco os que eu tive aulas diretamente,

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Analysis on Customer Satisfaction Dimensions in Peer-to-Peer Accommodation using Latent Dirichlet Allocation: A Case Study of Airbnb

Cited by 5 publications

References 11 publications

What Drives Airbnb Customers’ Satisfaction in Amsterdam? A Sentiment Analysis

What Drives Airbnb Customers’ Satisfaction in Amsterdam? A Sentiment Analysis

Improving peer-to-peer accommodation service based on text analytics

A influência da qualidade do serviço na hospitalidade domiciliar: um estudo do uso da Airbnb por turistas

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