Clustering and hotel room prices in Dubai

In this article, we study the influence of the room properties, hotel amenities, hotel location, and, more importantly, the characteristics of hotels in the surrounding area on the prices of hotel rooms. The effects of different determinants are estimated using the hedonic price model for a cross-section of 250 hotels in Dubai. In addition to the typical characteristics of hotels and hotel rooms such as hotel amenities, star rating, and room size, we include location-specific characteristics such as accessibility to public transportation, airport, and, more importantly, clustering variables to capture the effects of local competition and spillovers from surrounding hotels. Our results indicate significant and strong effects of accessibility to attractions, transportation, hotel’s star rating, and room size, as expected. Our estimations also indicate that local competition reduces the room price, and local quality spillover increases the room price, and both effects are predominantly limited to the hotel’s immediate surroundings. Our estimations indicate that having one more hotel in the immediate surroundings decreases the room price by about one percent, and an increase in the average quality of the hotels in the immediate surroundings by one star rating increases the room price by more than 20%.

Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. The website that is often used to view movie comments today is IMDB. Movie comments are many and varied on the IMDB website, we can see comments based on the star rating aspect. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users using the star rating aspect and will be classified using the support vector machine method (SVM). Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. SVM is very efficient for many applications in science and engineering, especially for classification (pattern recognition) problems. In addition to the SVM method, the TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the SVM model are 79% accuracy, 75% precision, and 87% recall. The SVM classification is also superior to other methods, namely logistic regression.

Availability, healthiness, and price of packaged and unpackaged foods in India: A cross-sectional study

Background: Vulnerable populations are the most prone to diet-related disease. The availability, healthiness, and price of foods have established associations with diet-related disease in communities. However, data describing this in India are sparse, particularly in urban slums and rural areas. Aim: To quantify and compare availability, healthiness, and price of packaged and unpackaged foods and beverages in India, and to identify opportunities to improve diets and health of vulnerable populations. Methods: Nutrition data and price were collected on foods and beverages available at 44 stores in urban, urban slum, and rural areas in four states in India between May and August 2018. Healthiness was assessed using the Australasian Health Star Rating system and product retail prices were examined. Comparisons in the findings were made across state, community area type, and adherence to current and draft Indian food labeling regulations. Results: Packaged foods and beverages ( n = 1443, 89%) were more prevalent than unpackaged ( n = 172, 11%). Unpackaged products were healthier than packaged (mean Health Star Rating = 3.5 vs 2.0; p < 0.001) and lower in price (median price per 100 g/ml: 13.42 Indian rupees vs 25.70 Indian rupees; p < 0.001), a pattern observed across most community area types and states. 96% of packaged products were compliant with current Indian labeling regulations but only 23% were compliant with proposed labeling regulations. Conclusions: Unpackaged products were on average much healthier and lower in price than packaged foods and beverages. Food policies that support greater availability, accessibility and consumption of unpackaged foods, while limiting consumption of packaged foods, have enormous potential for sustaining the health of the Indian population.

ACCU3RATE: A mobile health application rating scale based on user reviews

Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.

HRM and Employee Significant Behavior: Explaining the Black Box through AMO Theory

The purpose of this study is to explore the employees’ perceptions towards the role of HRM policies and practices in assisting them to perform the desired behavior in contributing to the organizational goals achievement. Employees’ perceptions are explored through their actual experiences with the implementation of HRM policies and practices. Interviews were conducted with employees in five different star-rating hotels to understand contextual factors that can be observed. Different perceptions on the role of HRM policies and practices in influencing employees’ performance are reported from the interviews. Instead of acting as a medium to transmit the message of their work expectations, employees view the HRM policies and practices as a common process happening in their organization and unrelated to their work demand. Therefore, findings of this study could light a torch of awareness for organizations to give more attention to the employees’ responses and feedback to minimize their dysfunctional behaviors that are detrimental to organizational achievement. Suggestions are given to increase the employee desired behavior relevant to the organizations from the perspective of AMO theory.

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

A systematic review of dialectical behavior therapy mobile apps for content and usability

Background The gap between treatment need and treatment availability is particularly wide for individuals seeking Dialectical Behavior Therapy (DBT), and mobile apps based on DBT may be useful in increasing access to care and augmenting in-person DBT. This review examines DBT based apps, with a specific focus on content quality and usability. Methods All apps referring to DBT were identified in Google Play and iOS app stores and were systematically reviewed for app content and quality. The Mobile App Rating Scale (MARS) was used to evaluate app usability and engagement. Results A total of 21 free to download apps were identified. The majority of apps (71%) included a component of skills training, five apps included a diary card feature. Most (76.19%) apps were designed to function without help from a therapist. The average user “star” rating was 4.39 out of 5. The mean overall MARS score was 3.41, with a range of 2.15 to 4.59, and 71.43% were considered minimally ‘acceptable,’ as defined by a score of 3 or higher. The average star rating was correlated with the total MARS score (r = .51, p = .02). Estimates of app usage differed substantially between popular and unpopular apps, with the three most popular apps accounting for 89.3% of monthly active users. Conclusions While the present study identified many usable and engaging apps in app stores designed based on DBT, there are limited apps for clinicians. DBT based mobile apps should be carefully developed and clinically evaluated.

Roadway Safety Assessment and Star Rating using iRAP along SH-11A (Jind-Kaithal) in Haryana

Figures cross 3,500 deaths and casualties on roads all around world every day in low- and middle-income countries and contribute about 90% of the 1.25 million road deaths. This number of road deaths is projected to increase by 50 percent by 2020. The compound problem for developing countries is caused by the rapid development of roads, irrespective of design or security, lack of attention to vulnerable road users and lack of a culture of road safety (i.e., safe behaviour, vehicle safety regulations, road safety policy, road safety assessment, and enforcement). This paper deals with the star rating and road safety assessment of State Highway-11A, Jind-Kaithal, Haryana though the section is straight but numerous causalities were reported on the route in recent years. ViDA, an online road safety assessment and star rating analysis tool is used to get the Star Rating Scores and Safer Roads Investment Plans.