Updated June 17, 2023
Difference between Text Mining and Natural Language Processing
The term “text mining” is used for automated machine learning and statistical methods used for this purpose. It is used for extracting high-quality information from unstructured and structured text. Data could be patterned in text or matching structure, but the semantics in the text is not considered. Natural language is what we use for communication. Techniques for processing such data to understand underlying meaning are called Natural Language Processing (NLP). The data could be speech, text, or even an image, and the approach involve applying Machine Learning (ML) techniques on data to build applications involving classification, extracting structure, summarizing, and translating data.NLP tries to handle all complexities of human language, like grammatical and semantic structure, sentiment analysis, etc.
Head To Head Comparison Between Text Mining and Natural Language Processing (Infographics)
Below is the top 5 Comparison between Text Mining and Natural Language Processing:
Key Differences between Text Mining and Natural Language Processing
Below is the difference between Text Mining and Natural Language Processing:
Application – Concepts from NLP are used in the following basic systems:
- Speech recognition system
- Question answering system
- Translation from one specific language to another specific language
- Text summarization
- Sentiment analysis
- Template-based chatbots
- Text classification
- Topic segmentation
Advanced applications include the following:
- Human robots understand natural language commands and interact with humans in natural language.
- Building a universal machine translation system is the long-term goal in the NLP domain
- It generates the logical title for the given document.
- Generates meaningful text for specific topics or for an image given.
- Advanced chatbots, which generate personalized text for humans and ignore mistakes in human writing
Popular applications of Text Mining :
- Contextual Advertising
- Content enrichment
- Social media data analysis
- Spam filtering
- Fraud detection through claims investigation
Development life cycle –
The general development process will have the following steps for developing an NLP system.
- Understand the problem statement.
- Decide what kind of data or corpus you need to solve the problem. Data collection is an essential activity for solving the problem.
- They are analyzing the collected corpus. What is the quality and quantity of the canon? According to the quality of the data and problem statement, you need to do preprocessing.
- Once done with preprocessing, start with the process of feature engineering. Feature engineering is the most critical aspect of NLP and data science-related applications. Different techniques like parsing and semantic trees are used for this.
- Having decided on extracted features from the raw preprocessed data, you must determine which computational technique is used to solve your problem statement; for example, do you want to apply machine learning or rule-based techniques? For modern NLP systems, advanced ML models based on Deep Neural Networks are used almost all the time.
- Now, depending on what techniques you are going to use, you should read the feature files that you are going to provide as input to your decision algorithm.
- Run the model, test it, and finetune it.
- Iterate through the above step to get the desired accuracy.
Basic steps like defining problems are the same for Text Mining applications as in NLP. But there are also some different aspects, which are listed below.
- Most of the time, Text Mining analyzes the text as such, which does not require a reference corpus as in NLP. In the data collection part, external corpus requirement is infrequent.
- Basic feature engineering for Text Mining and Natural Language Processing. Techniques like n-grams, TF – IDF, Cosine Similarity, Levenshtein Distance, and Feature Hashing are most popular in Text Mining.
- As mentioned earlier, system accuracy is measurable here, so Running, testing, and Finetune iteration of a model is relatively easy in Text Mining.
- Unlike the NLP system, Text Mining systems will have a presentation layer to present mining findings. This is more of an art than engineering.
- Future Work – With the increased Internet use, text mining has become increasingly important. New specialized fields, such as web mining and bioinformatics, are emerging. Currently, most of the data mining work lies in data cleaning and data preparation, which is less productive. Active research is happening to automate these works using Machine learning.
NLP is improving every day, but a natural human language is difficult to tackle for machines. We express jokes, sarcasm, and every sentiment quickly, and every human can understand it. We are trying to solve it using an ensemble of deep neural networks. Currently, many NLP researchers focus on automated machine translation using unsupervised models. Natural Language Understanding(NLU) is another field of interest that has a significant impact on Chatbots and humanly understandable robots.
Text Mining vs Natural Language Processing Comparison Table
Below are the lists of points that describe the comparisons between Text Mining and Natural Language Processing.
|Basis of Comparison||Text mining||NLP|
|Goal||Extract high-quality information from unstructured and structured text. Information could be patterned in text or matching structure, but the semantics in the text is not considered.||Trying to understand what is conveyed in natural language by humans- may be text or speech. Semantic and grammatical structures are analyzed.|
|Outcome||Explanation of text using statistical indicators like
1. Frequency of words
2. Patterns of words
3. Correlation within words
|Understanding what conveyed through text or speech like
1. Conveyed sentiment
2. The semantic meaning of the text so that it can be translated into other languages
3. Grammatical structure
|System Accuracy||A performance measure is direct and relatively simple. Here we have clearly measurable mathematical concepts. Measures can be automated.||Highly difficult to measure system accuracy for machines. Human intervention is needed most of the time. For example, consider an NLP system that translates from English to Hindi. Automating the measure of how accurately the system doing the translation is difficult.|
Both Text Mining vs Natural Language Processing try to extract information from unstructured data. NLP tries to get semantic meaning from all means of natural human communication, like text, speech, or even an image.NLP has the potential to revolutionize the way humans interact with machines.AWS Echo and Google Home are some examples.
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