Social media platforms provide a huge source of information about individuals, products, organisations and their interconnections. In many cases social media based textual communication contains instantly expressed feelings, attitudes, and impressions related to certain topics. Proper crawling and mining of social media provides valuable insights into how people currently think and talk about.
Real-time evaluation of huge data loads is often summarized under Big Data. However this term not only refers to large volumes, but also to high velocity, variety, and veracity of the data processed. The language technology behind TWIction, LingRep, is capable of dealing with all of these requirements.
TWIction picks out a single main feature of LingRep: the quantification of texts. In this process a tweeted texts is reduced to a vector of so-called orientations (e.g., Sentiment), which are subsequently used for aggregations, drill-downs and filtering. The key feature is customization of the analysis with a strong focus on the application needs.
Time-based, location-based and content-based aggregation and exploration enables detecting hot topics, triggering proactive reactions to avoid disadvantagous situations, and generating trend indicators.
TWIction was created as a simple-to-communicate and intuitive demonstrator. It contains elements that are interesting for many domains and application areas such as marketing intelligence, product tracking, or brand perception. Exploit these possibilities for your business and create additional added value!