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Text Analytics

Text analysis is a reasonable process of written communication.

In the context of client testing, text analysis means that you examine text written by or in the environment. You will find models and topics that interest you and then take action based on what you have learned. Text analysis can be done manually, but it is an inefficient process. For this reason, text analysis software has been developed that uses word processing and natural language processing algorithms to find meaning in large amounts of text.

Why do you need Text Analysis?

Emails, online reviews, tweets, call agent records, survey results, and other types of written feedback all have ideas for your customers. There is also a lot of information in recorded interactions that can be easily translated into text.

Text analysis is a way to open the meaning of all this unstructured text. This way you can find patterns and topics and know what your customers think. It expresses their wants and needs.

In addition, text analysis software can provide early warning of problems because it shows what customers are complaining about. Using text analysis tools, you can get valuable information from data that is not easily identified. Turning unstructured customer thinking into structured data that a company can use is not easy, but essential.

The API returns a list of strings that identify important discussion points in the introductory text. We use techniques from the sophisticated Microsoft Office Native Language Toolkit. Subtitles supported in English, German, Spanish, and Japanese. The API returns a list of strings that identify important discussion points in the introductory text.

We use techniques from the sophisticated Microsoft Office Native Language Toolkit. Subtitles supported in English, German, Spanish, and Japanese.

What all does the API do?

Sentiment Analysis: The API returns a numerical value between 0 and 1. Results near 1 indicate a positive mood, and a value near 0 indicates a negative mood. Emotional values are generated using classification techniques. Classifier input characteristics include n-grams, features produced by partial flow marks, and embedding words. This is supported in various languages.

Key Phrase Extraction: The API returns a list of strings that identify important discussion points in the introductory text. We use techniques from the sophisticated Microsoft Office Native Language Toolkit. Subtitles supported in English, German, Spanish, and Japanese.

Language Detection: The API returns open languages and numerical scores between 0 and 1. Results close to 1 give 100% certainty that the language identified is correct. A total of 120 languages are supported.

Named Entity Recognition: Find all names in the text, e.g. For example, organization, people and location. Topic links distinguish individual entities by linking text to additional information on the Web. For example, use it to determine whether the term "time" refers to "The New York Times" or "Times Square."