Infinite Analytics


Understanding How NLP and Text Mining Function To Further Businesses

by Team IA, September 2, 2021

One may have come across  natural language processing (NLP) and Text Mining (also known as Text Analytics) as extremely complex systems left to be deciphered by the data scientists. In reality, these are quite simple though the use of technology to apply them may be complex. Text Mining blends the processes of NLP and machine learning (ML) to derive meaning from text documents that are  unstructured. Infact, this is the technology behind turning several thousands of food reviews on food aggregator sites into specific recommendations. It is also used by workforce analysts, business analysts etc to improve productivity and further their business goals respectively. These examples are just the tip of the iceberg as Text Mining is actually capable of doing a lot more.

How A Text Analytics Engine Works

An unstructured document is broken into several parts before it begins the analysis. Infact, this is the point of beginning in most NLP features too. The basic steps to prepare a document for analysis include:

Language Identification: It is as simple as the heading. The language of the text is identified foremost. English, Hindi, Italian, all language have their own pecularities so language identification, though basic, ascertains the other aspects of the text analytics and is very crucial. The intelligence platform used supports many a language across even more logographies and alphabets.

Tokenization: After determining the language, it is broken down further into sentences, words, and phonemes. This act of breaking up the document is called tokenization. Tokens mostly consist of words and tokenization is specific to each language. For example the ‘matras’ of Hindi denote a token.

Sentence Breaking: After tokenization of the sentences it can be determined where the sentences terminate. The periods in the sentence also determine its boundaries. Similarly, one can even break sentences meant for social media.

Part of Speech Tagging: Ater the above three functions, PoS tagging is done to ascertain ‘part of speech’ in each token and is tagged likewise. PoS also ascertains the representation of a token i.e. verbs, adjectives, etc.

Chunking: Chunking or light parsing helps fragment the sentence into its components such as verb phrases, noun phrases etc. While PoS tagging indicates  giving PoS tokens to the text, Chunkin on the other hand involves giving those token to phrases.

Syntax Parsing: Syntax Parsing ascertains the sentence’s structure. It is essentially the diagram of the sentence and plays a pivotal role in sentiment analysis and NLP features.


Restaurants were closed until Covid…

Because restaurants were closed, Covid…

Restaurants were closed because Covid…

In the first sentence, the phrase  ‘Restaurants were closed’ is negative and ‘Covid’ is positive, while in the second sentence, ‘Restaurants were closed’ is negative and ‘Covid’ is neutral, but in the third sence, ‘Restaurants were closed’ and ‘Covid’ are both negative. With the use of advanced technology, Syntax Parsing, helps in understanding syntax like human beings.

Sentence Chaining: Sentence Chaining or Sentence Relation is the last step in preparing a sentence for the analysis. Different chaining tools are used to establish a connection between sentences. This chain flows through the document and once the relations between sentences are established, the sentiment scores are arrived at and accurate summaries can be derived for complex documents.

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by Team IA

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