![]() This process is known as Competitive Intelligence and it is crucial to quickly identify the opportunities provided by the market, to anticipate the decisions of the competitors as well as to learn from their faults and successes. A company can probe the Web to acquire and analyze information about the activity of its competitors. In commercial fields, the Web provides a wealth of public information. For example, collecting digital traces produced by users of social Web platforms such as Facebook, YouTube or Flickr is the key step to understand, model and predict human behavior. The availability and analysis of collected data is vital to understanding complex social, scientific and economic phenomena which generate the data. The importance of Web data extraction systems depends on the fact that a large (and steadily growing) amount of data is continuously produced, shared and consumed online: Web data extraction systems allow us to efficiently collect these data with limited human effort. Web data extraction systems find extensive use in a wide range of applications including the analysis of text-based documents available to a company (like e-mails, support forums, technical and legal documentation, and so on), Business and Competitive Intelligence, crawling of Social Web platforms, Bioinformatics and so on. The design and implementation of Web data extraction systems has been discussed from different perspectives and it leverages on scientific methods from different disciplines including machine learning, logic and natural language processing. Eventually, extracted data might be post-processed, converted to the most convenient structured format and stored for further usage. A Web data extraction system usually interacts with a Web source and extracts data stored in it: for example, if the source is an HTML Web page, the extracted content could consist of elements in the page as well as the full-text of the page itself. 2014) are a broad class of software applications that focus on extracting data from Web sources. Web data extraction systems ( Ferrara et al. For introduction to natural language techniques please see the Further reading references ( Liu 2011) (Chapter 11), ( Christopher, Prabhakar, and Hinrich 2008) (Chapters 12, 13, 15-17, 20), ( Aggarwal and Zhai 2012) (Chapters 1-8, 12-14) or other specialized books on natural language processing. Students that are interested in learning more about these techniques are encouraged to enrol in a Natural language processing course. However, the broader field of Web information extraction also requires the knowledge of natural language processing techniques such as text pre-processing, information extraction (entity extraction, relationship extraction, coreference resolution), sentiment analysis, text categorization/classification and language models. In this chapter we focus on Web data extraction (Web scraping) - automatically extracting data from websites and storing it in a structured format. Taking into account all the existing Internet-enabled devices, we can estimate that approximatelly 30 billion devices are connected to the internet ( Deitel, Deitel, and Deitel 2011). Today there are more than 3.7 billion Internet users, which almost 50% of the entire population ( Internet World Stats 2017). 11.4.2 Single imputation with prediction.11.1 The severity of the missing data problem.10.6 Putting it all together with Python.Estimating how performance will generalize.10.2 Commonly used prediction models and paradigms.10.1.1 The process of predictive modelling.8.5.3 Agglomerative hierarchical clustering.8.5.2 Determining the number of clusters.8.4 t-Distributed Stochastic Neighbor Embedding (t-SNE).6.3.4 Docker application example with multiple services.5.2 Descriptive statistics for bivariate distributions. ![]() 5.1.5 Testing the shape of a distribution.5.1 Descriptive statistics for univariate distributions.4.2.3 Modern Web sites and JS frameworks.4.1 Introduction to Web data extraction.3.5 Data dashboards - tooling and libraries.3.2.1 Preregistration - the future standard.1.3.2 Pure Python distribution installation.1.3.1 Anaconda distribution installation.
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