Text data

We provide all steps of data labeling service. The full data set will be labeled.

Text Analysis

Text analysis transforms the text into information using algorithms, analytical methods, and natural language processing. The analysis includes information retrieval, lexical analysis, pattern recognition, annotations, information extraction, and data mining techniques, including assessment and interpretation.

Sentiment analysis

Texts, sentences, and words can carry an emotional subtext that can be extracted and analyzed. A deep sentiment analysis examines the mood of the statement, i.e. whether the tone of the statement is neutral, positive or negative.


In the classification, categories and tags are assigned according to the given text content. The classification is related to, among others, sentiments’ analysis and intentions’ detection.

Named-entity recognition (NER)

The NER locates and classifies entities in the text into given categories, such as name, time, quantity, etc. For example: John [person] bought 2 [quantity] products from MaxMagic [company] in 2020 [date].

Datasets for NLP

The data is stored in a text format optimized for natural language processing (NLP).