Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Calendar based contextual information as an Internet content pre-caching tool

Motivated by the need to access internet content on mobile devices with expensive or non-existent network access, this paper discusses the possibility for contextual information extracted from electronic calendars to be used as sources for Internet content predictive retrieval (pre-caching). Our results show that calendar based contextual information is useful for this purpose and that calendar based information can produce web queries that are relevant to the users' task supportive information needs

OPINION MINING OF TRIPADVISOR REVIEW TOWARDS FIVE-STAR HOTELS IN BANDUNG CITY

New ways how and where customers look for recommendation and reviews have emerged. People nowadays tend to check the review and compare potential hotels before they go to make sure whether the hotel is suitable with their preferences or not. Travellers’ reviews, in the form of User Generated Content (UGC), have created more effective assessment system in comparison to the more traditional classifications such as hotel’s review in guidebooks. TripAdvisor as a market leader of review platforms represents the largest collection of UGC. It transforms not only how customers and hotel interact, but also the use of a new approach to knowledge as a source for customer insight. However, it is difficult for web users to read and understand contents due to the large number of reviews. The hotel management also finds it difficult to obtain any review to measure their service quality. This research aims to provide a comprehensive view of service quality for five-star hotels in Bandung city based on UGC. The dataset of online review is obtained from TripAdvisor. The author uses opinion mining technique to decompose user reviews into five dimensions to measure hotel service quality. This technique can help the hotel management to turn mountains of text opinion into fresh customer insights. The result of this research aims to provide an overview of five-star hotels in Bandung city what dimension of service quality that require attention to be improved. This overview can reduce the time required in understanding review contents for both the hotel management and web users. The overview indicates that each hotel has different dimensions that need more attention. Crowne Plaza, GH Universal, Hilton, InterContinental, Padma, Sheraton, and Trans have the least positive result for assurance in comparison to other dimensions, while Aryaduta, Novotel, and Papandayan have the least positive result for tangibles. This can be used for each hotel management to get customer insights that will help them deliver greater value for customers. Keywords: TripAdvisor, UGC, Service Quality, Hotel Review, Opinion Minin

Bench and Bar

Program of the annual convention of the Washington State Bar Associatio

What is the effect of sustainability practices by Norwegian hotels on their customer satisfaction?

Abstract Customer satisfaction is vital for the hospitality industry in today's competitive marketplace. This study aimed to see how sustainable hotel practices influenced customer satisfaction. Concerns about the environment are on the rise these days. Every business sets CSR and sustainability goals, and hotel customers keep an eye on them. As a result, hotels should adapt to new sustainable practices and deliver services so that the environment is not negatively impacted. Because hotels must listen to and react to the demands of their consumers, it is crucial to observe how youthful hotel customers engage with sustainable practices. The literature was aimed to discuss five variables for the study: sustainable hotel practices, hotel room price, CSR & Sustainable goals, hotel services, and customer satisfaction. The study looked at the effects of sustainability practices employed by Norwegian hotels on customer satisfaction. A random sample of 129 respondents who live in Norway and stay in hotels there was chosen to test the study findings. SPSS was used to perform Harman's Single Factor Test, Pearson correlation, and regression analysis. Sustainability practices have a favorable and considerable effect on consumer satisfaction. Furthermore, research has shown that modifications in sustainable practices do not affect hotel room pricing or costs. In reality, hotels increase their profit margins. The findings of this study will have practical benefits for sustainability, marketing, and human resources departments. This study will also be helpful to academia in understanding local hotel customer satisfaction resulting from sustainable practices

Sentiment Analysis: State of the Art

We present the state of art in sentiment analysis which covers the purpose of sentiment analysis, levels of sentiment analysis and processes that could be used to measure polarity and classify labels. Moreover, brief details about some resources of sentiment analysis are included