Best Text Analysis Software

Luminoso Analytics applies artificial intelligence (AI) and natural language understanding (NLU) to accurately analyze text-based data of any industry without long setup time and training. Luminoso Analytics allows customers to easily upload, process, analyze, and visualize batches or unstructured data streams. Compass can analyze all kinds of text-based data such as open questionnaire answers, call logs, chat bots or live chat, product reviews, articles, emails, NPS open formats. Data can be processed locally in 13 languages ​​including Chinese, Korean, Japanese and Arabic. Using Luminoso Analytics, the company displays key themes and concepts in the data, discovers and monitors trends over time, finds nuances and its underlying causes, and identifies key differences between metadata To do. Customers can use the insights discovered by Google Analytics in a variety of ways, including brand monitoring, analysis of churn and retention, problem detection and monitoring, and identification of root causes of NPS scores. This product is very flexible and can be deployed in standard or private cloud or on-premise solutions, or it can be integrated into the end-to-end platform via API.

Of course, transcripts play an important role in the use of text analysis or computer assisted qualitative data analysis software (CAQDAS) programs. These software solutions focus on "data as text" with an arbitrary number of built-in functions to help sort, count, search, plot, connect, browse, and provide context and collaboration . Analysts are usually instructed to start the analysis process by absorbing the contents of each transcript (through multiple readings) and then checking the transcripts of the associated code value text line by line. From there, analysts can process code using various program functions.

Even in free text search and sentiment analysis, most people understand the challenges of good text driven engines, but most people do not understand its complexity. Traditional software engineers often misunderstand that software is the focus of attention, but once the software is set up, a new data set will be extracted and another language will be used. But this is far from the truth. Natural language processing (NLP) scientists and computational linguists definitely believe that English is the most studied language in this field. The main driving force of this phenomenon is the availability of digital data and tag data. There is a problem if you need to extend certified algorithms to support other languages ​​for both academic and commercial reasons. The various operations of the algorithm do not apply to languages ​​other than English at all. For example, most European terms change gender based on specific circumstances.