Sentiment Analysis in TripAdvisor

Valdivia, Ana; Luzón, M. Victoria; Herrera, Francisco

doi:10.1109/mis.2017.3121555

Cited by 152 publications

(67 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Subsequently, textual analysis of the negative, neutral, and positive reviews was conducted using the qualitative analysis software Nvivo Pro 12 [10,69,70], which identifies key factors for the management of environmental issues by Swiss hotels using the presented process as a methodological support in the research conducted by Saura et al [3,49,71,72].…”

Section: Methodsmentioning

confidence: 99%

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

2018

View full text Add to dashboard Cite

Abstract:The main aim of this study was to identify the key indicators related to environmental management and sustainability of hotels as perceived by travelers during their trips. The methodology used was a sentiment analysis with an algorithm developed in Python trained with data mining and machine learning, with the MonkeyLearn library in the hotel industry sector under the eWOM model (e-Word of Mouth). The results with negative, positive and neutral feelings were submitted to a textual analysis with the qualitative analysis software Nvivo Pro 12. The sample consisted of the 25 best hotels in Switzerland according to Traveler's Choice from TripAdvisor ranking 2018 that draws from more than 500,000 reviews. For data extraction, we connected to the TripAdvisor API, obtaining a sample of n = 8331 reviews of the hotels that made up the ranking. The results of the study highlight the key factors related to environmental management detected by travelers during their stay in hotels and can be meaningfully used by managers or hotel managers to improve their services and enhance the value provided by their policies of sustainability and respect for the environment. The limitations of the present study relate to the size of the sample and the number of hotels included in the present analysis.

show abstract

Section: Methodsmentioning

confidence: 99%

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

2018

View full text Add to dashboard Cite

show abstract

“…While most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem [17] that requires tackling many NLP tasks, including word polarity disambiguation [18], subjectivity detection [19], personality recognition [20], microtext normalization [21], concept extraction [22], time tagging [23], and aspect extraction [24]. Sentiment analysis has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from financial [25] and political [26] forecasting, e-health [27] and e-tourism [28], user profiling [29] and community detection [30], manufacturing and supply chain applications [31], human communication comprehension [32] and dialogue systems [33], etc.…”

Section: Related Workmentioning

confidence: 99%

Multimodal sentiment analysis using hierarchical fusion with context modeling

Majumder

Hazarika

Gelbukh

et al. 2018

Knowledge-Based Systems

302

115

View full text Add to dashboard Cite

Multimodal sentiment analysis is a very actively growing field of research. A promising area of opportunity in this field is to improve the multimodal fusion mechanism. We present a novel feature fusion strategy that proceeds in a hierarchical fashion, first fusing the modalities two in two and only then fusing all three modalities. On multimodal sentiment analysis of individual utterances, our strategy outperforms conventional concatenation of features by 1%, which amounts to 5% reduction in error rate. On utterance-level multimodal sentiment analysis of multi-utterance video clips, for which current state-of-the-art techniques incorporate contextual information from other utterances of the same clip, our hierarchical fusion gives up to 2.4% (almost 10% error rate reduction) over currently used concatenation. The implementation of our method is publicly available in the form of open-source code.

show abstract

“…Sentiment analysis is a branch of affective computing research that aims to classify text (but sometimes also audio and video [8]) into either positive or negative (but sometimes also neutral [9]). Sentiment analysis has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from financial forecasting [10] and political forecasting [11], e-health [12] and e-tourism [13], community detection [14] and user profiling [15], and more.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised learning for big social data analysis

2018

View full text Add to dashboard Cite

In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different areas, with a growing impact on everyday life, such as in education, health, commerce and tourism, leading to an exponential growth in the size of the social Web. However, the distillation of knowledge from such unstructured Big data is, an extremely challenging task. Consequently, the semantic and multimodal contents of the Web in this present day are, whilst being well suited for human use, still barely accessible to machines. In this work, we explore the potential of a novel semi-supervised learning model based on the combined use of random projection scaling as part of a vector space model, and support vector machines to perform reasoning on a knowledge base. The latter is developed by merging a graph representation of commonsense with a linguistic resource for the lexical representation of affect. Comparative simulation results show a significant improvement in tasks such as emotion recognition and polarity detection, and pave the way for development of future semi-supervised learning approaches to big social data analytics

show abstract

Sentiment Analysis in TripAdvisor

Cited by 152 publications

References 17 publications

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

Multimodal sentiment analysis using hierarchical fusion with context modeling

Semi-supervised learning for big social data analysis

Contact Info

Product

Resources

About