Sentiment Analysis is a procedure used to determine if a chunk of text is positive, negative or neutral. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to the topics, categories or entities within a phrase.
Last week we introduced you a controversial topic: how to achieve higher accuracy while doing text categorization? We explained the two most popular approaches to approach this challenge, namely keyword matching and linguistics.
There are different approaches for text categorization, the most popular one is based on keyword matching however here at Bitext we use another approach that has made us unique, and it’s based on linguistics.
Text analysis technology has many applications outside from just feedback analysis. Call centers may seem out of date in this period where digital and email marketing are all around. However, in some industries like telecommunications or home utilities where many customers need to contact their service providers call centers can make order out of chaos.
Every business has only one reputation, and in a highly competititve market like the hospitality one, there is just one chance to give a good impression to your clients.
A recent report from Accenture suggested 87% of people now use a second screen while watching TV to comment not only shows but advertisement. Interactions in Social Media generate dialogue and analyzing all those coments can be overwhelming, but it's necessary to track the success of off-line campaigns. In this post we want to share with you how to measure these second screen interactions based in our experience with real clients.