Title: Using Natural Language Processing to Understand Us

Abstract:

The latest popular approach in natural language processing is the so-called word embedding (WE). The word embedding (WE) model is a neural network based on the distributional semantic model. The distributional hypothesis states that semantically similar words tend to have similar contextual distributions. In the WE context, if two words have similar vectors, then they have the same distribution. An application of WE based on periodical is called temporal word embedding or dynamic word embedding (DWE).
We have explored the use of WE and DWE to mine lifestyle, sentiment and evolution of trends and policy. In the work of WE on Malaysian Twitter corpus, we explore the possibility of viewing Malaysia's lifestyle on where they spend most of their time for social meetings and analyse the sentiment of a public figure.

Biography:

Sabrina Tiun is a senior lecturer at the Faculty of Information Science and Technology (FTSM) in Universiti Kebangsaan Malaysia (UKM), Malaysia. Her research interests range from natural language processing, computational linguistic, and speech processing to social science computing. She teaches several courses in natural language processing and data science at both undergraduate and postgraduate levels. She also actively and successfully supervises postgraduate students for master and PhD level in natural language processing and speech processing. Currently, she is the head lab of Asian language processing (ASLAN) lab and the teaching and learning coordinator for the Centre of Artificial Intelligence Technology at FTSM.