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Wolfram () used Twitter data to train a Support Vector Regression (SVR) model to predict prices of individual NASDAQ stocks, finding ‘significant advantage’ for forecasting prices 15 min in the future.In the biosciences, social media is being used to collect data on large cohorts for behavioral change initiatives and impact monitoring, such as tackling smoking and obesity or monitoring diseases.
Either comparable facilities need to be provided by national science foundations or vendors need to be persuaded to introduce the concept of an ‘educational license.’Clearly, there is a large and increasing number of (commercial) services providing access to social networking media (e.g., Twitter, Facebook and Wikipedia) and news services (e.g., Thomson Reuters Machine Readable News). We start by discussing types of data and formats produced by these services.
Although we focus on social media, as discussed, researchers are continually finding new and innovative sources of data to bring together and analyze.
Three illustrative areas are: business, bioscience and social science.
The early business adopters of social media analysis were typically companies in retail and finance.
Currently, social media data is typically either available via simple general routines or require the researcher to program their analytics in a language such as MATLAB, Java or Python.
As discussed above, researchers require: There are an increasing number of powerful commercial platforms, such as the ones supplied by SAS and Thomson Reuters, but the charges are largely prohibitive for academic research.The majority of social media resources are commercial and companies are naturally trying to monetize their data.As discussed, it is important that researchers have access to open-source ‘big’ (social media) data sets and facilities for experimentation.Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services.This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms.They either give superficial access to the raw data or (for non-superficial access) require researchers to program analytics in a language such as Java.Social media data is clearly the largest, richest and most dynamic evidence base of human behavior, bringing new opportunities to understand individuals, groups and society.So when considering textual data analysis, we should consider multiple sources (e.g., social networking media, RSS feeds, blogs and news) supplemented by numeric (financial) data, telecoms data, geospatial data and potentially speech and video data.Using multiple data sources is certainly the future of analytics.In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London.The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business.