Understanding How NLP and Text Mining Function To Further Businesses
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.
Subscribe to our weekly newsletter and write to us at firstname.lastname@example.org to know more about how we can help you grow your business.
It’s most obvious in the digital media space, from click buys to personalized web experiences. For marketing, the AI journey has just kick-started, while in the tech sector it has been applied for a while now. We are still at an early stage where inroads are being made into AI content via chatbots and even some explanatory content creation but what will make anyone jump up and embrace it is when we will start seeing a lot of mainstream content being created by AI.
Prior to joining Infinite Analytics, Richard served as the CFO of CrowdFlower, COO and CFO of Phoenix Technologies, as a member of the board of directors and chairman of the Audit Committee at Intellisync, and previously as CFO and executive vice president strategy and corporate development at Charles Schwab.
Pravin Gandhi has over 50 years of entrepreneurial operational and investing experience in the IT industry in India. He was a founding partner of the first early stage fund India - INFINITY. Subsequently a founding partner in Seedfund I & II. With over 18 years of investing experience, he is extensively well networked in investment and entrepreneurial scene and is an active early stage angel investor in tech & impact space. Pravin holds a BS in Industrial Engineering from Cornell University, and serves on the board of several private corporations in India. He is on the board of SINE, IIT Mumbai Incubator.
Puru has his Masters in Engineering and Management from MIT. Prior to MIT, he worked with Fidelity Investments building electronic trading products and high volume market data processing applications. He has completed his BE from VJTI, Mumbai.
Deb Roy is Professor of Media Arts and Sciences at MIT where he directs the MIT Center for Constructive Communication, and a Visiting Professor at Harvard Law School. He leads research in applied machine learning and human-machine interaction with applications in designing systems for learning and constructive dialogue, and for mapping and analyzing large scale media ecosystems. Deb is also co-founder and Chair of Cortico, a nonprofit social technology company that develops and operates the Local Voices Network to surface underheard voices and bridge divides.
Roy served as Executive Director of the MIT Media Lab from 2019-2021. He was co-founder and CEO of Bluefin Labs, a media analytics company that analyzed the interactions between television and social media at scale. Bluefin was acquired by Twitter in 2013, Twitter’s largest acquisition of the time. From 2013-2017 Roy served as Twitter’s Chief Media Scientist.
Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).
Akash co-founded IA while studying for his MBA from MIT. Prior to MIT Sloan, he co-founded Zoonga. Before this, Akash was an engineer with Oracle in Silicon Valley. He has completed his M.S from University of Cincinnati and B.E from the College of Engineering, Pune.