Question

Most organizations are just scratching the surface of what they can learn and accomplish through the analysis of unstructured text. Opportunities for large and small businesses, as well as applications, are expanding. How can text analytics be applied to solve today’s business problems? What are some of the challenges organizations face when implementing text analytics solutions? Write your responses in detail with examples. Be sure to identify the source of your example in your posting. Your initial post should be of minimum 400 words.

Answer 1

Text Analysis has the ability to automate the customer support, improve the client experience, and analyze the feedback given by the consumers and their outputs. Text Analytics is a type of AI that is simple and easy to use. It can likewise complete a ton to help our business forward.

Also, the great aspect regarding text analysis is, it’s all over. Regardless of industry, organizations and people want to settle on better-informed business decisions based off identifiable and better knowledge. With progressions in Text Analysis, businesses would now be able to mine text to insights and improve their service or offering to flourish in their respective market area. Let’s look at some of the applications of Text Analysis or NLP which has helped many businesses to improve their services and the products:

Auto Suggestions on Search

Auto Suggestions on Search is another kind of Text Analytics feature that many business companies use consistently and have nearly generally expected when looking for something. It has happened because of the great players like Google, who have been utilizing the feature in their search engine for quite a long time. This kind of element is similarly as supportive on company sites.

Sentiment Analysis Feature

To modify the sales and marketing strategy, sentiment analysis helps businesses by knowing about the clients what they feel about your brand. This kind of innovative idea, also known as opinion mining, has been originated from social media analysis and is fit for analyzing news and blogs giving a value to the content (positive, negative or neutral). The Text Analytics currently algorithms go as far as recognizing emotions, for example, irritated, glad, miserable or grumpy. Obviously, with exact tools like these marketers currently, have everything necessary to create remarkable strategies and settle on well informed decisions.

TV Audience Analysis & Advertising

TV programs and live broadcast events are possibly the most discussed topics on Social Media / twitter. Advertisers and TV program makers can both profit by utilizing Text Analytics in two particular ways. If producers can get a conception of how their group of spectators ‘feels’ about specific characters, settings, storylines, emphasized music and so on, they can make changes in a campaign to mollify their viewers and consequently increase the crowd size and viewers ratings. Publicists can research into social media networking platform streams to analyze the viability of product placement and commercials advertised during the breaks.

Assessment of Creditworthiness

By utilizing Text Analytics, Banks in developing nations would now be able to assess the creditworthiness of customers with practically zero financial records. Irrespective of whether these customers have never utilized credit, the majority of them despite everything use cell phones, browse on the web and take part in various exercises that leave a lot of digital imitations. Text Analysis algorithms analyze geolocation data, social media activities, behavior of browsing to infer bits of knowledge into their routines, habits, peer systems, and quality of their relations. By assessing a lot of customer related factors, the software creates a credit score remarkably prescient of client’s further activity. Access to client information is only allowed on acquiescence and the information can never be transferred to outsiders.