About DiscoverText

DiscoverText is a cloud-based text analysis tool that helps legal, consumer services, education, government and other sectors gain insights into information from various sources including emails, text files, Facebook, Twitter and more. It lets users utilize API to search or capture data from live feeds and machine learning merged with human intervention to de-duplicate or classify large quantities of unstructured data into structured units.

Key features of DiscoverText include filtering, redaction, de-duplication, topic modeling and reporting. The solution allows businesses to redact sensitive information, annotate data and attach memos to datasets or documents for coworkers, improving collaboration among team members. Additionally, users can group near-dupli...


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Supported Operating System(s):

Web browser (OS agnostic), Windows 10

107 Reviews of DiscoverText

Average User Ratings

Overall

4.57 / 5 stars

Ease-of-use

4.0

Value for money

4.5

Customer support

4.5

Functionality

4.5

Ratings Snapshot

5 stars

(68)

68

4 stars

(33)

33

3 stars

(5)

5

2 stars

(1)

1

1 stars

(0)

0

Likelihood to Recommend

Not likely

Very likely

Showing 1 - 5 of 107 results

December 2017

Wasim from University of Sheffield

Verified Reviewer

Industry: Higher Education

Time Used: More than 2 years

Review Source: Capterra


Ease-of-use

5.0

Value for money

5.0

Customer support

5.0

Functionality

5.0

December 2017

Superb cloud-based software tool with powerful text analytics with social media integration.

It allowed me to conduct research that otherwise would not have been possible including both industry and academic projects.

Pros

The ability for those from the social sciences to be able to import and/or retrieve social media data, including historical data from Twitter, and analyse the data in order to answer research questions. This is because other tools may require a computer science background. Consequently, DiscoverText has been used in answering important social science questions leading to peer reviewed outputs. Over the years I have seen many tools appear and slowly wither away, however, DiscoverText has stood the test of time and has been growing in popularity. DiscoverText is not limited to academic uses and has a number of neat uses in the commercial world. A useful feature applicable to the commercial domain is the ability to retrieve and/or import data from Twitter and identify influential Twitter users, with the additional ability to use machine learning to sift influential users into different groups. For example, a football club may be interested to find out whether influential users are fans of the club or whether it is opposition fans causing a storm. To the best of my knowledge, no other tool is capable of doing this with this level of accuracy.

Cons

To be fair this is not a limitation of DiscoverText per se, as this is a restriction from Twitter, but there is a limit to how many units of tweets can be exported per day. This is not a major issue because there are enough features in DiscoverText that you may not necessarily need to export the data. This is particularly true with a recent integration with NodeXL which provides the ability to export directly to a format supported by NodeXL.

December 2018

Christine from Culligan International

Verified Reviewer

Company Size: 1,001-5,000 employees

Industry: Environmental Services

Review Source: Capterra


Ease-of-use

4.0

Value for money

5.0

Customer support

5.0

Functionality

5.0

December 2018

A much better way to scrape data than learning how to code an API

I'm a PhD candidate who straddles the Humanities and Social Sciences, so I use DiscoverText as a research tool.

Pros

My review of DiscoverText is a bit limited because I'm really only using it for Twitter. That said, the features are incredible. I know a bit about coding, but the prospect of learning JSON to use Twitter's API was doable but daunting. When I came across DiscoverText I was so pleased to find a way to search, use, and categorize Twitter data that made sense and would save me A LOT of time. I didn't anticipate getting access to so much useful metadata that was easy to navigate and use, so I was pleasantly surprised. The built-in bucket and dataset features are great ways to organize the massive amount of Twitter data that can be collected. The ability to code the data with peers within DiscoverText is also super useful. I really can't exaggerate how many features DiscoverText has that I didn't think I would need but have used to improve the quality of my scholarship.

Cons

The software has many features that I didn't find on my own, so the UI could be improved a bit. That said, the one-on-one tutorial that the founder provides helps mitigate this issue. The tutorial videos are helpful too! You'll just have to be prepared to set aside a few hours to really learn the program.

Response from Texifter

Replied January 2019

Dear Christine, It is really hard to express how inspirational a review like this is. You have really made our day. We are looking at 2019 trying to decide if this is the year to build v2 of a 9 year old interface. Thanks for embracing buckets & datasets; this was a tough sell to some folks over the years, but they are critical to User success. We are very grateful you took the time to write this generous review. Please write us if we can do anything for you. Thanks, ~Stu

April 2018

Katarina from Dublin City University

Time Used: Less than 6 months

Review Source: Capterra


Ease-of-use

2.0

Value for money

5.0

Customer support

3.0

Functionality

2.0

April 2018

Great for analyzing social media data- just not offline documents.

Pros

If you need to analyze data from social media and survey monkey- it's a great tool. You can search for content by keywords and the data drops in per the chosen frequency. It breaks down keywords and phrases to a list in order of use- where you can drill into each word or phrase to see where it's used and also toggle between different ways of displaying the results.

Cons

I got the trial version to see if it would suit my purpose; I required a tool to analyze and cluster data from articles and other sources but just couldn't get it to work. If the trial period had been longer than 3 days ( I thought I signed up for 30- it's not very clear) I might have had time to figure it out. Better instructions would have helped. The instructions tell you what the features are, not why you need to use them which is not helpful for novice users.

Response from Texifter

Replied September 2018

Katarina, Sorry for the confusion about the length of the free trial. It was 30-days for many years and we changed it to 3 only recently. Please send a request to info@texifter.com and I will send you a 6-month license. For details about the features, we suggest you review some of the support materials: https://texifter.zendesk.com/hc/en-us As to why use the tools, perhaps review the tutorials: https://discovertext.com/tutorials/ You might also find some answers as to why use the tools here, in the 200+ academic citations of the tools: https://discovertext.com/publications/ Finally, I am available for 1-1 web trainings: https://calendly.com/discovertext So, I think you may not have fully tried to use all the customer support options. We work very hard to make sure newcomers get comfortable quickly. Stu

January 2018

Hossein from Farhangiyan University

Verified Reviewer

Time Used: Less than 6 months

Review Source: Capterra


Ease-of-use

5.0

Value for money

5.0

Customer support

5.0

Functionality

5.0

January 2018

Honestly,I can say DiscoverText makes analyzing social data not only easier, but also more enjoyable

It provides me more opportunities for working on my projects. Using it, I have access to many ways for doing research on social media data which have not before.

Pros

First, it is so easy to learn and use. Moreover, the DiscoverText founders provided some helpful tutorials and educational videos which are so handy and helpful. This software allows users to makes several datasets of one project. This enables a researcher to work on multi-dimentions of a certain project needless to create different ones. Furthermore, you can create a sample of your data very easily by making a dataset. Its buckets are very interesting also. Additionally, you can make some clouds of data by using cloud explorer feature. Finally, Clustering option is great! it makes working on big data easy and shows the main trends in them quickly.

Cons

As I can say, sometimes users may get confused by many links and pages. So, maybe finding what you want becomes difficult and you have to try some ways. Another con, in my point of view, is the obscurity of metadata meanings and algorithms. I cannot understand what some of the means and how they are calculated. Furthermore, I think some of metadata can be presented in some more useful ways. But at all, I should confess the metadata explorer is a great ability!

Response from Texifter

Replied January 2018

Thanks Hossein for an excellent review. We are preparing a new blog post now with a Metadata Dictionary for Gnip Twitter data. We agree that some of the fields are a bit confusing and we hope this new blog post will make the meaning of some of the fields more transparent.

January 2018

Jim from George Mason University

Verified Reviewer

Industry: Higher Education

Time Used: More than 2 years

Review Source: Capterra


Ease-of-use

3.0

Value for money

5.0

Customer support

4.0

Functionality

5.0

January 2018

We have been using the software to study twitter conversations on immigration going back to 2013.

Easy data capture

Pros

It's ability to capture tweets and now the capacity to export to NodeXL gives us two tools that we use together to study the content and structure of immigration conversations onTwitter.

Cons

At times navigating the menus is counter-intuitive as is some of the terminology. Archives, buckets, datasets all kind of run into one another.

Response from Texifter

Replied January 2018

Dear Jim, Thanks for your and the generosity of your Tweets. We really appreciate that! I'd like the opportunity to visit GMU to make the case that archives, buckets, and datasets are essential parts of the text analytics methods we have engineered. Please email info@texifter.com if you would like to host a free workshop. Briefly: - Archives are raw data. - Buckets are subsets of raw data. - Datasets are coded by humans. Most projects proceed from 1 or more archives, to many buckets, to a series of codeable datasets. For example: - Collect 100,000 #metoo tweets - Deduplicate the archive - Create a bucket of seeds and singles - Search the bucket for key terms - Create a new bucket with results - Create a dataset and code it for relevance - Train a relevance classifier - Apply the classifier to new archive samples - Repeat as needed The key point is that raw data is messy in the archives, cleaner in buckets, and fully refined and classified in datasets.