Specify the topics of the selected documents.Identify in which documents a specific chemical compound is mentioned.Gain insights from a collection of documents about chemical compounds.Demonstration of a practical example of a KNIME workflow in order to:.
An overview of the range of insights and knowledge that can be mined from text, providing business cases to highlight this.The team of data scientists at Redfield has put together a webinar about text mining that addresses these challenges. Overcoming this challenge is important for organizations to stay competitive. However, extracting the valuable information that is potentially hidden in the textual data and depicting relations among the textual data explicitly, for example by visually representing key attributes of the text in relation to industry standards can pose quite a challenge. Natural Language Processing (NLP), topic modeling, sentiment and network analysis - all text mining techniques - can be used effectively in areas such as marketing (analysis of online customer interactions), politics (analysis of political speeches to ascertain party alignment), technology (assessment of COVID-19 app acceptance), research (publication biases), and electronic records (e.g., email, messaging, document repositories), spam filtering, fraud detection, alternative facts detection, as well as Q&A. Text mining is an efficient data analysis technique to use if you need to not only get a quick sense of the content of specialized documents, but also understand key terms and topics, and reveal hidden relations.Īll kinds of different analysis opportunities are opened up by text mining techniques.
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