Text analytics refers to the variety of research techniques used to interpret texts. Any piece of writing is text, such as books, emails, or transcriptions. They can also be multimodal objects such as a film, an image, or a place. Either way, results work to produce qualitative or quantitative results. This is all dependent on the methods used and the overall aim of the study.
Analyzing writing from all sorts of fields is an important part of research. Cultural, media, social, and literary studies aim to connect the writing to a specific framework. Specific methods include content analysis (some specific to composition and context), thematic analysis, and discourse analysis. Text mining is an advanced way to conduct this type of research, analyzing large datasets of text to draw the same relational conclusions.
Many text mining techniques involve computing software, systems, and applications. Read the top three reported by RankRed and learn more about the technologies applied in various academic and professional settings.
1. Natural Language AI by Google Cloud (8.6/10)
– Analyzes text documents (email, chat, and product reviews).
– Classifies documents into over 700 categories.
– Supports multiple languages (Chinese, Japanese, and Spanish).
2. Amazon Comprehend (8.1/10)
– Detects over 100 languages.
– Extracts information from complex documents such as legal guardianship certificates.
– Removes personal information.
3. Levity (8.3/10)
– No-code platform running on Artificial Intelligence (AI).
– Uses machine learning models to organize text.
– Build, train, and test custom AI commands without ever coding.
Many other applications are described here as some of the best to use. Generally, they allow organizations to frame, locate, and extract information from large, unstructured data. How might you use any of these text-analyzing applications?