
Data processing regards analysis of various types of data, including numerical data, signals, texts, pictures, videos, etc. This paper focuses on defining and studying various tasks of text analytics following the typical processing pipeline. Sources of textual data are introduced and related challenges are discussed. Along with the process of text analytics, examples are presented to demonstrate how text analytics should be carried out. Finally, potential applications of text analytics are given including sentiment analysis and automatic generation of content.
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