Foodbot: A goal-oriented just-in-time healthy eating interventions chatbot

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

On analyzing geotagged tweets for location-based patterns

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

CO2Vec: Embeddings of co-ordered networks based on mutual reinforcement

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

On detecting maximal quasi antagonistic communities in signed graphs

National Research Foundation (NRF) Singapor

Talent flow analytics in online professional network

Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ

JobSense: A data-driven career knowledge exploration framework and system

National Research Foundation (NRF) Singapor

Automatic classification of software related microblogs

Abstract—Millions of people, including those in the soft-ware engineering communities have turned to microblogging services, such as Twitter, as a means to quickly disseminate information. A number of past studies by Treude et al., Storey, and Yuan et al. have shown that a wealth of interesting information is stored in these microblogs. However, microblogs also contain a large amount of noisy content that are less relevant to software developers in engineering software systems. In this work, we perform a preliminary study to investigate the feasibility of automatic classification of microblogs into two categories: relevant and irrelevant to engineering software systems. We extract features from the textual content of the microblogs and the titles of any URLs mentioned in the mi-croblogs. These features are then used to learn a discriminative model used in classifying relevant and irrelevant microblogs. We show that our trained model can achieve a promising classification performance. I