Meet the researcher: Text-based data, machine learning and creating change
By: Qut Science
Tags: Statistics, Data, Data Science, Mathematics, Computer Science

Associate Professor Richi Nayak is dedicated to data and data-led outcomes.

Specifically, her research focuses on analysing text data through data mining and machine learning, developing algorithms to explore large repositories of text-based data to identify insights and trends, create profiles and generate reports.

The applications for this kind of research are endless. Nayak has worked across domains from agriculture to energy, health to transport infrastructure, marketing to online dating, providing bespoke advanced machine learning techniques for clients who need to understand their user base.

Dr Richi Nayak develops sophisticated machine learning algorithms to investigate text-based data sources. Image: QUT Science and Engineering

Straight from the source

Sources for text-based data analysis are varied, and the algorithms Nayak develops adapt to these sources: scanning vast quantities of online information to compile reports based on published papers; scraping millions of social media posts; or analysing thousands of digitised documents saved from a dusty box in a government storage room.

From the data, these algorithms identify trends, patterns and features that can then be analysed further, or can be used to influence policy and change at organisational or institutional levels.

“There’s an abundance of data available, and you’d struggle to make sense of it on an individual level, like a single tweet or an individual news story,” Nayak said.

“We collect social media data and engage in a process called topic modelling, or clustering, where we identify users who talk about the same subject or theme. We can then look at influential users within those communities, look at the attitudes in those communities, and any hierarchies within the subgroup.

“From that, we can build a more complete profile of attitudes and contexts within the social media landscape, which clients can then act on in a much more informed way.”

The data that is fed into the machine learning algorithms is key to producing a successful model, and for text-based analysis this data takes on an additional layer of complexity as the analysis goes beyond occurrences and into semantics: words have meaning, and the machine needs to have an understanding of how language works.

“The machine doesn’t know that words are correlated in any way, so it needs to learn that before it can show thematic trends it comes across in the data,” Nayak says.

“Right now my research is focused on developing ways to help the machine understand the inherent nature of the data — what its original context is, and what the shape of that is, so that it can build a base of understanding.”

The shape of data to come

The idea that data has a distribution and a shape is key to understanding computational geometry, a key area of Nayak’s research.

“Think of each word in a collection of text as a data point: some of those data points appear together more frequently than others in language,” Nayak explained.

“For example, when ‘machine’ and ‘learning’ appear together, the meaning of the phrase ‘machine learning’ is different from the individual words when they appear independently.

“Within the text that’s being searched — the search space — the machine can measure the distance between those data points to build an understanding of correlation, creating a geometric ‘map’ of the data.

“The sheer volume of data we work with means that it’s too complex to incorporate into a real-world, visual medium, so the geometric shape is just defined by code and used internally by the algorithm.

“A sound machine learning algorithm can identify those correlations accurately and build a basic level of semantic understanding, and from there identify common trends, themes and profiles within the data sets.”

These maps, called graph maps, can be visualised as a series of nodes with links between them — or, for a very simplified version, as a word cloud.

“This is one simple method of showing quantitative text data in a way that’s easy for everyone to visualise and understand,” Nayak said.

For the majority of Nayak’s work, though, the algorithms she creates are incredibly complex, capable of parsing billions of tweets to develop meaning.

“This means that based on words’ occurrences and their context, a deep learning method can build a text classifier to identify, for example, a misogynist tweet.

“That complexity can tell us amazing stories, though — the information is out there, and the programs we develop give us a way to understand it.”

Inspired to create change

Although the application of data science can creep towards anthropology, Nayak’s domain is firmly rooted in data collection, analysis and validation.

“As data scientists, we want to identify trends, validate our models, and produce something that’s clear and intelligible for our industry partners, while social scientists want to understand the demographic drivers beneath the data,” Nayak said.

“That said, I’m inspired to have an impact and make changes in society through my work.

“Many of my projects are applied research to solve an industry problem or provide insight to a sociological issue, so I get to see the data analysis translate to real outcomes that affect real people.”

Nayak’s work also helps identify areas of potential bias that might impact the way we interpret certain data sets.

“Often our work can highlight missing data that the domain expert isn’t even considering or may be biased about.

“I can approach their problem without bias and show them how to use their own data to change their practice. We improve their data collection methods to give a more holistic picture of the areas they’re working in, to make changes that are more meaningful for greater parts of the population.”

A data-driven future is ideal for scientists like Nayak.

“Most organisations and governments have so much data available, but they’re just not making use of it,” Nayak said.

“Applying machine learning and artificial intelligence techniques can make organisations smarter about their data, better informed by their data, and help them make decisions based on information that’s meaningful and real.”

More information

Explore more research at QUT’s Science and Engineering Faculty.

Learn more about Associate Professor Richi Nayak.