Difference between Text Mining and Natural Language Processing

Text Mining and Natural Language Processing (NLP) are both fields within the broader domain of computational linguistics, but they serve distinct purposes and employ different methodologies:

Text Mining

Text Mining 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. 

Natural language processing (NLP)

Natural language processing (NLP) 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.	Aim is to extract useful insights from structured and unstructured text.	Aim is to understand what is conveyed in speech.
2.	It deals with the conversion of textual content into data which is further analyzed.	Its goal is that computer systems can understand human languages or text.
3.	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.
4.	To perform tasks, it does not consider semantic analysis.	It considers Syntactic analysis and semantic analysis for performing tasks.
5.	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.
6.	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.
7.	It does not require human intervention.	To process data, sometimes it requires human intervention.
8.	It produces the pattern and frequency of words.	It produces structure like grammatical structure.
9.	Performance measure is direct and relatively simple.	Highly difficult to measure system accuracy for machines.
10.	It can be used to monitor social media.	It can be used in website translation.
11.	It primarily works with unstructured text data such as documents, webpages and emails.	It works with various forms of text, speech and other forms of human language data.
12.	It’s application include sentiment analysis, document categorization, entity recognition and so on.	It’s application include machine translation, chatbots and so on.
13.	It is less dependent on the specific language being analyzed.	It is highly dependent on language, as various language-specific models and resources are used.
14.	Rapid Miner and KNIME are some tools used.	NLTK and spaCy are some tools used.
15.	It is less context-sensitive as it focuses on surface-level features.	It is highly context-sensitive and most often requires understanding the broader context of text provided.