1. 文本挖掘：
它的目标是从文本中提取重要的数字索引。因此，使文本内容中包含的事实可用于一系列算法。可以提取信息以导出包含在文档中的摘要。它本质上是一种人工智能技术，包括处理来自各种文本内容文档的信息。许多深度学习算法用于对文本进行有效评估。在这种情况下，信息以非结构化格式保存。
2.自然语言处理(NLP)：
它的重要性在于使计算机系统能够识别自然语言。但这不再是一个方便的挑战。计算机可以识别信息的结构化结构，如电子表格和数据库中的表格，但是人类语言、文本和语音塑造了非结构化的数据类别，PC 难以识别，这就是为什么需要NLP 出现。
文本挖掘和自然语言处理的区别：
| 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. |