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This paper aims to investigate customers’ experiences with Airbnb by text-mining customer reviews posted on the platform and comparing the extracted topics from online reviews between Airbnb and the traditional hotel industry using topic modeling.

This research uses text-mining approaches, including content analysis and topic modeling (latent Dirichlet allocation method), to examine 1,026,988 Airbnb guest reviews of 50,933 listings in seven cities in the USA.

The content analysis shows that negative reviews are more authentic and credible than positive reviews on Airbnb and that the occurrence of social words is positively related to positive emotion in reviews, but negatively related to negative emotion in reviews. A comparison of reviews on Airbnb and hotel reviews shows unique topics on Airbnb, namely, “late check-in”, “patio and deck view”, “food in kitchen”, “help from host”, “door lock/key”, “sleep/bed condition” and “host response”.

The topic modeling result suggests that Airbnb guests want to get to know and connect with the local community; thus, help from hosts on ways they can authentically experience the local community would be beneficial. In addition, the results suggest that customers emphasize their interaction with hosts; thus, to improve customer satisfaction, Airbnb hosts should interact with guests and respond to guests’ inquiries quickly.

Hotel managers should design marketing programs that fulfill customers’ desire for authentic and local experiences. The results also suggest that peer-to-peer accommodation platforms should improve online review systems to facilitate authentic reviews and help guests have a smooth check-in process.

This study is one of the first to examine consumer reviews in detail in the sharing economy and compare topics from consumer reviews between Airbnb and hotels.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

This paper aims to disclose the evolution of pendulum hardness of two-component acrylic polyurethane coatings during the cure process and attempts to describe the quantitative relationship between pendulum hardness and curing time. These findings are helpful for the study of fast curing acrylic polyurethane coatings.

The pendulum hardness method was used to monitor the hardness of two-component acrylic polyurethane coatings during curing. The quantitative relationship between pendulum hardness and curing time can be obtained with Avrami equation.

The evolution of coating pendulum hardness can be divided into three stages. By using the Avrami equation that explained the influence of both the acid value and the curing temperature on the drying speed of hydroxyl acrylic resin, the evolution of coating pendulum hardness during curing can also be accurately described.

It should be noted that the physical meaning of the Avrami exponent, n, is not yet clear.

The results are of great significance for the development of fast-curing hydroxyl-functional acrylic resins, with the potential to improve the drying speed of the coatings used in automotive refinish.

It is novel to divide the pendulum hardness into three stages, and, for the first time, the Avrami equation is utilized to describe the evolution of coating pendulum hardness during curing.