The Word Cloud in ATLAS.ti can be used to search in any part of your project: documents, quotations, codes, and groups.
In addition to seeing what everyone said, it’s also interesting to compare different groups of respondents (such as those who play the game and those who don't). For instance, you might want to focus on only the adjectives present in the data to see how respondents described the game. If you really want to unpack precisely what is being said, you can have ATLAS.ti filter the results according to specific parts of speech. ATLAS.ti makes it even easier to understand which words are mentioned by inferring base forms, or automatically combining all variations of the same word (such as “play”, “played”, and “playing”). From the Minecraft survey data, you can already see that participants talked mostly about feeling, thinking, people, and the word “great” came up a lot. Use stop lists, go lists, and set thresholds to focus on what matters. Even before opening and reading each person’s response, you can see exactly what was said in a Word Cloud that shows all words that appear in the text along with the frequency of each word. The essence of making sense of qualitative data is analyzing words: What did participants talk about? What is going on in a given piece of text? The moment you have data in your hands, you can already start exploring and uncovering potential patterns with the Word Cloud in ATLAS.ti Desktop.įor example, you may have just collected survey data asking people to evaluate a video game, such as Minecraft. Understand exactly what’s being said with Word Cloud The ATLAS.ti AI Lab has been working long and hard to develop NLP features that work with researcher’s needs, and ATLAS.ti now offers a wide range of tools that can unlock new potentials for any methodology and any research question.
#ATLAS TI SOFTWARE#
Programmers and software developers have built artificial intelligence (AI) technologies that incorporate natural language processing (NLP), and researchers can finally harness AI-driven tools to facilitate their qualitative data analysis. However, these times are now falling behind us as technology continues improving at exponential rates! For a long time, it was extremely difficult to design digital technologies that could effectively automate analysis of rich data such as interviews, focus groups, surveys, comments, and other kinds of text. But there is a catch: analyzing qualitative data can be very time consuming! This is simply because there is so much information present – both in terms of explicit, surface-level details as well as subtle nuances that lie behind what is being said. Qualitative research bears great potential to unleash innovative insights, because data is collected in an open-ended manner that allows people to express themselves and talk about whatever they feel is relevant. How can AI tools help with qualitative data analysis?
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