Every human being is interested to know their chances of getting sick in the future. This is where artificial intelligence scientists steps in. With the combination of machine learning, natural language processing and text analytics scientists are working to find out some latest concepts and technologies in biomedicine. Each term is then given a score, which, based on the technology's analysis, identified its predicted rate of incline. So based on that, few predictions have been made for 2016.  To know more, follow: http://www.science20.com/news_articles/ai_may_tell_us_whats_going_to_be_big_in_science_this_year-165373

Business executives know that text analytics have immense benefit. But, sometimes don’t understand all the concepts associated with it. Jerri Ledford (Analyst with Studio B), writes in her article about some of the most important terminology surrounding text analytics. Understanding these terms will help executives. These are – Annotation, Latent Dirichlet Allocation, Lexical Chain, Machine Learning, Probabilistic Latent Semantic Analysis, Natural Language Processing, Topic Modeling, etc. Read more at: http://it.toolbox.com/blogs/inside-erp/text-analytics-15-terms-you-should-know-surrounding-erp-70728

The value of data is no longer unknown to the business world.  Both structured and unstructured data are important because they are unprocessed raw materials which go into analysis. Many business leaders and IT professionals prefer structured data, while it is currently being observed that unstructured data also has a lot to offer. Unstructured data coming from various social network sites help business leaders to gauge customer sentiments and grievances. It also helps to reduce costs and adapt to a changing market situations. Sometimes there can be challenges handling unstructured data such as collection, organization, integration and analysis. text analytics, auto taxonomy generation, auto tagging and other techniques are vital when it comes extracting value from unstructured data. To know more read: http://www.cio.com/article/2941015/big-data/solving-the-unstructured-data-challenge.html

There is no doubt about the revolution that big data has brought to the way business is done. But, most of the talk has been around the structured data. It has been increasingly becoming clear that the potential of big data can be truly understood if we take up the challenge of harvesting unstructured data. Jonathan Buckley, senior vice president of marketing at Qubole, in an article in Smartdatacollective emphasizes that if businesses want to remain relevant and profitable then it’s the right time to turn their attention to text analytics. The most important advantage that text analytics have is that it provides with a much larger sample of customer sentiment and extract data which is otherwise not quantifiable. But all of this boils down to having the right technology. For more on this follow the link http://www.smartdatacollective.com/jonathanbuckley/329383/text-analytics-next-frontier-big-data

Text data is the best answer in understanding customer feedback. It is best for answering the why and what questions. Text Analytics is becoming mainstream as interest is pouring in. Sources include call center notes, email messages, social media etc. Following Fern Halper's TDWI article, 5 fundamentals include: 1. Text analytics involves analyzing unstructured data to extract relevant information.
2. Extracting various information in the form of keywords, entities, concepts and sentiments
3. Considering taxonomy, while dealing with specific vocabularies.
4. Integrating text data with traditional data, depends on the kind of data
5. Text data is not as accurate as statistical techniques
To know more http://tdwi.org/Articles/2015/06/23/5-Text-Analytics-Fundamentals.aspx?Page=2

Big data can also be applied in the field of humanities specially history. With advancement in text analytics, historians can now go through a huge number of historical documents and discover patterns and trends from the past. These then can be used as evidence to check if theories are correct. Research students analyzed a huge number of novels using big data analytics which allowed them to leverage insight on what the semantics implied about the society. Given the nature of historical documents, it is difficult to digitalize and apply text analytics to those. Thus data analytics is being considered by historians now. Big data when applied to history will help bring information scattered over the world thus helping historians to gain insight and predict the future. Read more at: https://channels.theinnovationenterprise.com/articles/data-looking-at-the-past

Through predictive algorithms, text analytics in Big Data can discover a wide range of happening things about a business. On the positive side, text analytics can be fed a lot of unstructured text. Customer experience management, brand monitoring, compliance and brand management can be done with the help of text analytics. This was of great use during the US election as it found out reasons behind interested voters.  Though on the negative side, text analytics tool get confused for words which has a dual meaning in the same culture or for different cultures. Hence, it should be made such that it can stand up successful for different sentiments too which many are trying to establish now. Read more about this article by Elliot Pannaman (International Events Director) at: https://channels.theinnovationenterprise.com/articles/maximizing-business-performance-with-text-analytics

Even in this multichannel world, customers still prefer to call in and hence voice channel is critical to a business. These days’ businesses are coming to a standstill when it comes to listening to conversations that flow through their voice channel as they are stuck on sampling and high cost manual monitoring. A recent survey showed that it is important to link online marketing to offline sales.
Mining for the truths and hidden meanings from these conversations is crucial. Previously automatic data mining of the voice channel was unattainable. But now with conversation analysis it is possible. Being in the century of customers, the ability for the company representatives to understand the customer, be empathic and deliver fast solution is essential.
Thanks to conversational analysis or text mining, it is now possible to extract data from conversations, to automate and process this data in real-time and at scale. Thus listening to and analyzing conversations helps the representatives to build better relationships and in turn provide customers with the experience they expect.
Sigmaway provides text mining solutions for businesses. Contact us at contact@gosigmaway.com for a proof of concept.
Read more at: http://smartdatacollective.com/calljourney/304151/are-you-listening-your-conversations

Providing good customer service is a challenge for most businesses and the trends are ever changing. Customers embark on an online journey whenever they buy something. The customer data - a string of emails, conversations across the web and social channels, leave a large digital footprint. Measuring and analyzing this information can help brands narrow down their marketing efforts and investments. Irrespective of the nature of the website, sentiment analysis is crucial. It can tell you whether your customers are frustrated, indifferent, or impressed and suggest measures to that accord. Monitoring customer engagement, referral traffic and time spent on pages can help in understanding the buyers’ journey. Thanks to Big Data, we can work based on intelligence rather than guesswork. Read more at: http://www.forbes.com/sites/danielnewman/2015/06/09/big-data-and-the-buyers-journey-measuring-the-invisible/

Internet search engines like Google, Yahoo or Bing are like large knowledge repositories - they provide you with any information you want. But, there is a condition - you have to type right keywords. Everyone one of us knows this.

How many times have you got the right search results you expected? It can be never 100% correct, even though you thought of the keywords carefully. Here comes the accuracy of the search engines, and improving this accuracy between what you type and what results you get is of utmost importance to the search engine providers. Behind the wall, lots of text analytics are involved.  What you normally type in the search box is an unstructured content and the search engine's job is to analyze, extract meta data and index it to convert the content into a structured one. The query is made upon the indexed data, after which the results are shown to you. What makes search engines different is the ability to add context, extract meta data from unstructured content, and index them so that accurate search results are shown to the extent possible.

What text analytics are involved here? Know them here: http://www.business2community.com/big-data/text-analytics-important-search-0889955#!PueWW .

Stuart Rose, global insurance marketing manager at Cary, talks about how Insurance companies gather piles of text based data everyday about customers’ detail. It also addresses the different patterns used to explore unstructured information and analyze them. Text Analytics helps in understanding the details contained in the unstructured sources. The areas where text analytics can positively impact on insurer profitability are – Subrogation, Fraud Detection and Brand Reputation.

For more information please follow

http://www.insurancetech.com/business-intelligence/3-insurance-business-applications-for-te/240165269