Text Mining vs Natural Language Processing

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. Information 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 is collectively called as Natural Language Processing (NLP). The data could be speech, text or even an image and approach involve applying Machine Learning (ML) techniques on data to build applications involving classification, extracting structure, summarizing and translating data.NLP trying 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 who 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 –

For developing an NLP system, the general development process will have the following steps

• Understand the problem statement.
• Decide what kind of data or corpus you need to solve the problem. Data collection is a basic activity toward solving the problem.
• Analyzing collected corpus. What is the quality and quantity of the corpus? 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 important aspect of NLP and data science-related applications. Different techniques like parsing, semantic trees are used for this.
• Having decided on an extracted features from the raw preprocessed data, you are to decide which computational technique is used to solve your problem statement, for example, do you want to apply machine learning techniques or rule-based techniques?. For modern NLP systems, almost all time advanced ML models based on Deep Neural Networks are used.
• Now, depending on what techniques you are going to use, you should read the feature files that you are going to provide as an input to your decision algorithm.
• Run the model, test it and fine-tune.
• Iterate through the above step to get the desired accuracy

For Text Mining application, basic steps like define problems are the same as in NLP. But there are also some different aspects, which is listed below

• Most of the time Text Mining analyzes the text as such which does not require a reference corpus as in NLP. In data collection part external corpus requirement is very rare.
• Basic feature engineering for Text Mining and Natural Language Processing. Techniques like n-grams, TF – IDF, Cosine Similarity, Levenshtein Distance, Feature Hashing is most popular in Text Mining. NLP using Deep Learning depends on specialized neural networks call Auto-Encoders to get a high-level abstraction of text.
• Models used in Text Mining can be rule-based statistical models or relatively simple ML, models
• As we mentioned earlier, system accuracy is clearly measurable here so Run, Test, Finetune iteration of a model is relatively easy in Text Mining.
• Unlike the NLP system, there will be a presentation layer in Text Mining systems to present findings from mining. This is more of an art than engineering.

• Future Work – With the increased use of the Internet, text mining has become increasingly important. New specialized fields such as web mining and bioinformatics are emerging. As of now, a majority of 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 getting better every day but a natural human language is difficult to tackle for machines. We express jokes, sarcasm and every sentiment easily and every human can understand it. We are trying to solve it using an ensemble of deep neural networks. Currently, many NLP researchers are focussing on automated machine translation using unsupervised models. Natural Language Understanding(NLU) is another field of interest now which has a huge impact on Chatbots, and humanly understandable robots.

Text Mining and Natural Language Processing Comparison Table

Below are the lists of points, 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 text or speech. Semantic and grammatical structures are analyzed.
Tools	Text processing languages like Perl Statistical models ML models	Advanced ML models Deep Neural Networks Toolkits like NLTK in Python
Scope	Data sources are documented collections Extracting representative features for natural language documents Input for a corpus-based computational linguistics	The data source can be any form of natural human communication method like text, speech, signboard, etc Extracting semantic meaning and grammatical structure from the input Making all level of interaction with machines more natural for human
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, which translates from English to Hindi. Automate the measure of how accurately system doing translation is difficult.

Conclusion

Both Text Mining vs Natural Language Processing trying to extract information from unstructured data. Text mining is concentrated on text documents and mostly depends on a statistical and probabilistic model to derive a representation of documents.NLP trying to get semantic meaning from all means of human natural 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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