Business Applications of an Advanced Text Analysis Engine

Text is an important part of communication. It can be found everywhere around us. Text?has become a vital element of day-to-day life as the internet's usage and popularity has grown. The data accessible for gathering insights has multiplied over the years, from social media chats between friends and family to reviews online and grievances between consumers and brands.

Companies and people, regardless of industry, desire to make better-informed business decisions based on actionable and measurable data. Companies can now mine text for insights and expand their services or offering to thrive in their industry thanks to improvements in Text Analysis engines like 3RDi Search, Algolia and Swiftype. The purpose of this article is to discuss some of the business applications of text analysis engines.

Sports Trading

Football is one of the most major sports to gamble on, especially in Europe (soccer). Top sports traders get information from the mainstream press and have a comprehensive understanding of the game and its local politics with the help of insights by a Text Analysis tool. You could extract meaning from local Twitter feeds using a Text Analysis engine that understands Spanish, providing you insight into what local fans are saying about their team.

Financial Trading

A financial trader, like a sports trader, can benefit greatly from having a local perspective on what is going on. Domain-specific sentiment analysis and classification can be extremely useful in this situation. Traders in specific markets have their own different vocab, similar to how fans have their own distinct vocab dependent on the sport. Spoken Language and Intent Recognition Understanding services for recognizing user intents from short sentences can assist traders decide what to trade, how much to trade, and how fast to trade.

Fraud

Fraudulent conduct may be identified far more rapidly when those investigating can connect the links faster, whether it's workers claiming fake compensation or a motorist providing a bogus home location. A Text Analysis tool makes this possible. For example, in the latter scenario, the guilty party may provide an address that is related with other claims, or the driven vehicle may have been engaged in prior claims. The ability to record this data saves time for the insurer and gives them more insight into the situation.

Lead Generation

Taking prompt action on a source of Social Media data?can be used to both retain and win new clients, as was the case with the VOC application. For example, if a?user tweets that they are interested in this particular product or service, text analysis engines can detect this and pass the information along to a sales representative, who can then chase the prospect and turn them into a customer.

Recruitment

Text analysis engine could be employed in the recruitment process during both the search and selection phases. The most basic application of a Text Analysis tool in this scenario would be determining a potential hire's skills. Identifying candidates before they become engaged in the job market adds substantial value to the recruiting sector.

For example, knowing if someone posts about disliking their workplace or expressing an interest to work in a different profession, larger/smaller company, or different place would be extremely useful. Once you've located a suitable candidate, you can utilize Text Analysis engine to assess their suitability based on what others have said about them. Using news and blog articles, forum postings, and other sources to analyze potential workers could be beneficial.

Review Sites

Expedia, for example, has millions of evaluations from travellers all around the world on its website. Given the site's nature and the fact that its customers want a stress-free experience, having to comb through dozens of ratings to find a place to stay can be a huge turnoff. Text analysis engines can be used to create tools that summarize various properties in two to three-word phrases.