I have been reading feedback and reviews of the software we develop for more than a decade. Sometimes this is a painful process. While we value all feedback, sometimes it is delivered in a manner that can give one pause.
Recently we joined the software review platform Capterra. This RaaS offer (Reviews as a Service) is actually pretty cool. Dubbed “The Smart Way to Find Business Software,” Capterra authticates and incentivizes reviewers. While we are new to the service, it did help us collect more than 30 useful reviews in a short time. Many of the comments are extremely valuable as we update and improve the software. We are the most-reviewed text mining software on Capterra, and one of the top 5 reviewed accross all software categories.
This morning, I read this review, and I had to share it as widely as possible. The anonymous but verified reviewer goes into detail about how “DiscoverText has completely changed the way in which I conduct twitter analysis.” The reviewer writes:
“As a postgraduate researcher, this software has offered me a completely new alternate to how I will conduct my research into Twitter bots. Whilst I originally planned to mine the data from the Twitter API utilising an algorithm developed in Python, I now plan to use DiscoverText to gather the data which I require for the purposes of my dissertation. The software is extremely intuitive and no technicalities are involved, unlike the Python route! To mine Twitter data, you simply open a stream after creating a project and highlight what queries you want the platform to mine and save them onto the online platform. The data which you have then mined comes in a completely understandable format in comparison to an algorithm where the data will be unstructured! This will save me an astounding amount of time for my dissertation as everything I want to do and more comes as standard with DiscoverText.”
We have argued for years that academics trust DiscoverText because it reduces the time spent on project and data management, and it frees up time for research design, experimentation, testing, and analysis.