Text Processing & Machine Learning (TPML)

Data has always been a critical component of any business model. It has been used mostly to detect causal trends between two overt variables for the purpose of optimizing an objective.  In recent times, the extent of digitalization has made it easy to collect abundance of data which has resulted in gigabytes of data not necessarily between variables with overt relations.  This abundance of data has resulted in the discipline of Data Science which deals with extracting latent information embedded in seemingly unrelated or loosely related variables.  Text processing goes a step further and deals with techniques to extract information embedded in texts expressed in a natural language.  It is challenging as natural language is unstructured, however it can be successfully used for a huge range of applications.  Some of the sample projects from the research group are:

  1. Social media text mining
    • Location mining from Tweet messages
    • Bullying detection on social media platforms
    • Chronological topic tracking in Tweet messages
  2. Text Classification
    • Sentiment detection in movie reviews
    • Sentiment detection in hospitality related service reviews
  3. Recommender Analytics
    • Recommend movie/wine based on keywords
  4. Conversational Agents
    • Use of “chatbots” to interact with users for information dissemination
    • Natural text generation for conversational agents
  5. Document/s summarization
    • Collate/present information from annual reports
  6. Semantic Web
    • Designing linked data from NZ Government’s released open data initiative.