Natural Language Processing
- What is and why text Analytics required ?
NLP is a technology that allows machine to understand human language or unstructured data in form of text. As human we all can understand the tenses(past, present, future etc.), we can understand the meaning of sentence(making a clear differentiation between words when they are together in sentence), recognizing the entity( As of now we can relate entity with Noun, will study in more details in coming topics). So NLP lets individuals use their normal speech and writing patterns to communicate with computer systems in more convenient way and providing meaningful fact with textual data
Why text Analytics required : It is required as in coming era of changing technology day by day, we have gathered lots of lot of unstructured data. So why we all are so greedy about this data, as data in form of text which may provide some meaningful information or important KPI which can be really very very beneficial. Okay think in this way, data in twitter which have tweets about government policies, let us say demonetization in India can actually lead to results how the people of country are taking that government step. Customer review in page of Amazon can lead about Product feedback, seller feedback and in fact Amazon feedback. So you can think how important and crucial this data is in each and every fields
Text Analytics: Areas of Application
- Social Media Analytics
- Banking and Loan Processing
- Insurance Claim Processing
- Help Desk or Ticketing System/Call Centers
- Cognitive Science
- Security and Counter Terrorism
- Government and Government Policies
- Computational Social Science
- and many more
So this is overview of NLP, in the coming Blogs we will focus on three main pillars for text Analytics or “steps” generally undertaken on the journey from data to meaning. This can be divided in three parts which are further divided in sub parts. We will cover industry example with proper explanation of Algorithm used
- Lexical Processing : Extraction of Raw form of text and then using some techniques like Stopword removal, Bag of Words, Stemming etc.
- Syntactic Processing : Understanding the language of text. Understanding the grammar and Parsing technique such as POS tagging etc.
- Semantic Processing: Understanding the meaning of text. LDA, LSA, PLSA techniques etc.
So stay tuned we will be covering all this in coming weeks.…. Regards Chetan/Kamal