Function: web intelligence platform; Etymology: composed from web (as in World Wide Web) and lyzard (as in analyzer). 1 : (broadly) collects and integrates intelligence from (social) media and digital content; can be identified by its speed, accuracy, scalability. 2 : tracks topical trends and helps gain a deeper understanding of information flows by means of visual and semantic technologies.
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including screenshots and a product feature comparison.
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webLyzard collects and analyzes big data repositories gathered from a range of electronic sources including news media, corporate Web sites, blogs, social networking platforms, etc. Within and across these sources, state-of-the-art Web intelligence tools reveal flows of relevant information between stakeholders through trend analyses, benchmarks and customized reports. The extracted knowledge supports marketing and public outreach managers, strategists and chief information officers alike in their efforts to monitor emerging trends and improve their organizations’ online communication strategies.
“Given the intense attention that climate change attracts these days, key questions for leading organizations such as NOAA are how different stakeholders perceive this issue, how public media react to new scientific insights, and how journalists present climate science knowledge to the public. The metrics and visualizations of the webLyzard platform have proved invaluable for addressing these questions”
David Herring, AAAS Fellow and Director of Communications and Education at the Climate Program Office
of the U.S. National Oceanic and Atmospheric Administration (NOAA)
By uncovering patterns and trends in stakeholder communication, webLyzard helps to engage with target audiences and shows how communication is received, understood, and remembered. The system not only identifies how often, in what media, and where information is presented, but also provides a real-time account of topics that target audiences associate with an organization or its products and services. Positive or negative sentiment, an important indicator for understanding the impact of external communications, is detected automatically and in multiple languages.