The aim of establishing hotels is to provide a service activity to its customers with the aim of making a profit. So, for that the cancellations are a key perspective of inn income administration since their effectiveness on room reservation systems. Cancelling the reservation eliminates the outcome. Many expected factors affect this problem. By knowing these factors, the hotel management can make a suitable cancellation policy. This project aims to create an API that can provide a function to predict if a reservation is most likely to cancel or not. That API can integrate with the hotel management systems to evaluate each reservation process with the same parameters. To do this, the study starts by defining the factors using Chi test, correlation to find the effective variables, and coefficient of the variables in the linear regression. And the results that have been found for the factors are: is repeated guest, previous cancellations, previous bookings not cancelled, required car parking spaces, and deposit type. For API function, the intercept and coefficients have been used from the logistics regression model to create a scoring function. Scoring function can be calculated by the sum of the factors multiplied by their coefficients in addition to the intercept. This score is to be evaluated as a probability later using the logistic function.

Thousands of active people on social media daily share their thoughts and opinions about different subjects and different issues. Many social media platforms used to express the feeling or opinion and at top of it is Twitter. On Twitter, many opinions are expressed in many fields such as movies, events, products, and services; this data considered a valuable resource for companies and decision-makers to help in making decisions. This study was based on using a hybrid approach to extract the opinions from an Arabic tweet to measuring service providers' reputation. In this study, the Saudi telecom companies used as a case study. This research concentrates on determining peoples' opinions more accurately by utilizing the Retweet and Favorite. The number resulting from positive and negative tweets after applying the polarity equation was used to estimate reputation scores. The result indicated that the STC company represents a high reputation compared to other companies. The proposed approach shows promising results to expand existing knowledge of sentiment analysis in the domain of measure reputation.