![]() ![]() This paper, therefore, develops a framework to probe into the innovation ecosystem that involves science, technology, and business ecosystems and the connections between them, especially for emerging economies. However, innovation ecosystem needs to be specifically attended when facing the fasting-developing emerging industries that closely link science, technology, and business. Governments and industrialists are keen to foster innovation ecosystem in order to systematically cultivate favourable environments to encourage local innovators to create knowledge and capture business value. Innovation ecosystem is receiving increasing attention worldwide. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time. ![]() The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Finally, we propose two approaches based on neural network language models and social network analysis to derive firm-level information from the extracted web data.Ĭurrent disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. Web-based studies also have to contend with distinct outliers and the fact that low broadband availability appears to prevent firms from operating a website. We find, inter alia, that the use of websites and websites’ characteristics (number of subpages and hyperlinks, text volume, language used) differs according to firm size, age, location, and sector. the population of firm websites in Germany), which has as yet not been studied rigorously in terms of its qualitative and quantitative properties. We apply this tool in a large-scale pilot study to provide information on the data source (i.e. For this purpose, we present an easy and free-to-use web scraping tool for large-scale data retrieval from firm websites. Using the example of innovation in firms, we outline a framework for extracting information from firm websites using web scraping and data mining. Comes with examples and various tools to help you analyze and create the necessary regular expressions.Nowadays, almost all (relevant) firms have their own websites which they use to publish information about their products and services.Supports using multiple regular expressions to match and extract the data you want.Adjust the speed of crawling to accomodate your needs versus server load.The website crawler features lots of options.Grab product and inventory lists and link to them from your own website.Integrate scraped website data for various data analysis algorithms and tools.travel, hotels, weather and stock quotes. Using a website scraper to extract data can be useful to a wide range of tools and services: Filter which URLs to scrape data from and generate CSV and SQL files ready to be imported anywhere, e.g. Mix and mash scraped website data to create new and innovative mashup website services or data analysis tools. Scrape websites to extract data for use in web services and similar. A1 Website Scraper lets you scrape Data from Websites into CSV & SQL
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