Differences in competitors' market influence due to market structure: Evidence from Japanese gambling market.

This study investigates how differences in the market structure between the Japanese horse racing and Keirin1 racing markets affects the influence exercised by high-turnover operators (major operators) in both markets on low-turnover operators (minor operators) in those markets.2 In the horse racing market structure, there are few competitors, and the difference in turnover3 between major and minor operators is large. In contrast, in the Keirin racing market structure, there are many competitors, and the difference in turnover between major and minor operators is small. Panel analysis results show that in horse racing, operators with low turnover are significantly affected by those with high turnover, while in Keirin racing, operators with low turnover are less affected by competitors with high turnover. The results not only indicate that firms are affected differently by competitors due to the market structure but also suggest that this has an impact on market segmentation policies and firms' marketing efforts.

A data package for abstractive opinion summarization, title generation, and rating-based sentiment prediction for airline reviews.

Customer reviews are valuable resources containing customer opinions and sentiments toward the product. The reviews are informative but can be quite lengthy or may contain repetitive information calling for opinion summarization systems that retain only the significant opinion information from the review. Abstractive summarization is a form of text summarization that generates a summary mimicking a human-written summary [1]. When pretrained language models are finetuned for abstractive review summarization, there usually occurs a problem known as the 'domain shift', because the source and target domains exhibit data from varying distributions [2]. This issue results in performance degradation of the model at the target end. This paper contributes a data package comprising of an annotated abstractive summarization dataset (annotated_abs_summ) of airline reviews having 500 reviews and abstractive summary pairs, a dataset (review_titles_data) consisting of 7079 reviews and review title pairs for review title generatioon or domain adaptive training [3] to address the domain shift problem for abstractive opinion summarization and, an annotated reviews dataset (annotated_sentiment) for rating-based sentiment classification. All datasets have been collected from the Skytrax Review Portal via web scraping using Python programming language. The datasets have several potential use cases. The abstractive summarization dataset can serve as a benchmark dataset for airline review summarization. The dataset for domain adaptive training can be used as a standalone dataset for review title generation. The dataset for sentiment analysis is multipurpose having columns like user rating and recommendation value, that can be used for statistical analysis like finding correlation between these data items as well as for other Natural Language Processing (NLP) tasks like predicting rating or recommendation value from the customer reviews. The datasets can be extended using various data augmentation techniques [4,5]. Moreover, the datasets are related and can be collectively used to develop a multi-task learning model [6] for better learning efficiency and improved performance.

Generation of Chow Parameters and Reduced Variables Through Nearest Neighbor Relations in Threshold Networks.

Generation of useful variables and features is an important issue throughout the machine learning, artificial intelligence, and applied fields for their efficient computations. In this paper, the nearest neighbor relations are proposed for the minimal generation and the reduced variables of the functions in the threshold networks. First, the nearest neighbor relations are shown to be minimal and inherited for threshold functions and they play an important role in the iterative generation of the Chow parameters. Further, they give a solution for the Chow parameters problem. Second, convex cones are made of the nearest neighbor relations for the generation of the reduced variables. Then the edges of convex cones are compared for the discrimination of variables. Finally, the reduced variables based on the nearest neighbor relations are shown to be useful for documents classification.

News consumption patterns on Twitter: fragmentation study on the online news media network.

The development of news media and social media has radically changed the way the public consumes information. This study explores the structure of online news media networks in three countries, namely Indonesia, Malaysia and Singapore, to investigate the phenomenon of fragmentation in the news consumption pattern on social media. Based on the results of the three network indicators used in this study, it can be concluded that the structure of online news media networks in Indonesia and Malaysia shows a tendency of fragmentation. In contrast, this study did not find sufficient evidence that the phenomenon of fragmentation was occurring in the Singapore media network. In-depth analysis on each formed media cluster shows that online news media in Indonesia and Malaysia tend to group based on similarity in market segments, regions or political alignments.