Difference between Text Mining and Natural Language Processing

1. Text Mining :
Its goal is to extract significant numeric indices from the text. Thus, make the facts contained in the textual content available to a range of algorithms. Information can be extracted to derive summaries contained in the documents. It is essentially an AI technology that includes processing the information from a variety of textual content documents. Many deep learning algorithms are used for the effective assessment of the text. In this, the information is saved in an unstructured format.

2. Natural language processing (NLP) :
Its importance is to make computer systems to recognize the natural language. That’s no longer a handy challenge though. Computers can recognize the structured structure of information like spreadsheets and the tables in the database, however human languages, texts, and voices shape an unstructured class of data, and it receives challenging for the pc to recognize it, and that is why the need for NLP arises.

Difference between Text Mining and Natural Language Processing :

S.No. Text Mining Natural Language Processing
1. It deals with the conversion of textual content into data which is further analysis. Its goal is that computer systems can understand human languages or text.
2. To process data, it uses various types of tools and languages. It uses high-level machine learning models to process data and for producing output.
3. To perform tasks, it does not consider semantic analysis. It considers Syntactic analysis and semantic analysis for performing tasks.
4. The main source of data in text mining includes massive docs. In this, there can be multiple sources of data such as signboards, speech, etc.
5. In this, we can measure the system performance and its accuracy easily as compared to NLP. In this, to measure system performance is quite difficult as compared to Text Mining.
6. It does not require human intervention. To process data, sometimes it requires human intervention.
7. It produces the pattern and frequency of words. It produces structure like grammatical structure.
8. It can be used to monitor social media. It can be used in website translation.

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