The Center for Business and Social Analytics leverages business and social big data to help us understand human behavior.
In today’s digital era, the abundance of data generated through social media platforms, peer- to-peer applications, and emerging technologies like the Internet of Things (IoT) and 5G networks, has created opportunities for uncovering valuable insights through data analytics. To meet these demands, HKUST’s Center for Business and Social Analytics (CBSA) is on a mission to study timely business and social problems by harnessing the power of big data.
Drawing on the expertise of faculty and researchers from a range of disciplines, CBSA engages in a variety of projects. These include constructing Hong Kong’s first forward-looking tourism index using forecasts powered by artificial intelligence, developing sentiment indices based on sentiment expressed in financial reports and earnings calls of listed companies, exploring the impact of how sentiments are shaped on the Internet and assessing how law and policy influence sentiment.
Unlike traditional methods such as opinion polls and surveys, CBSA leverages business and social big data to generate insights and analysis. Researchers can tackle complex issues by extracting useful information from sources such as social media posts and interpersonal exchanges to assess a crowd’s sentiment and behavior at a given time.
Data analytics has become a key field in the understanding of human behavior, and it can also address societal challenges. Such advances allow the university to drive meaningful change.
Senior Associate Dean of HKUST Business School and Director of CBSA Professor HUI Kai-Lung (second from right) and Adjunct Associate Professor of HKUST Business School and Advisor of CBSA Professor HE Chao (first from right) spearhead the development of the Wisers-HKUST Tourism Index at HKUST.
Project Showcase: Hong Kong’s First Forward-looking Tourism Index Supporting Tourism with AI-Powered Forecasts
CBSA has launched the Wisers-HKUST Tourism Index in collaboration with Wisers Information Limited (Wisers) in support of Hong Kong’s tourism growth. As Hong Kong’s first forward-looking tourism index leveraging artificial intelligence (AI)-powered forecasts, the project enables industry stakeholders to better gauge near-term tourism outlook for pre-emptive policy and business planning, thereby contributing to the tourism industry’s long-term development.
The Index offers forecasts of key tourism metrics, such as overall tourism sector outlook, mainland visitors’ arrivals, hotel occupancy, and hotel average daily rate, based on a predictive model of mainlanders’ intentions in traveling to Hong Kong. The model was constructed using leading-edge AI technologies, such as natural language processing and machine learning, to analyze over 10 million data points from social media platforms, online travel agencies, and travel forums in Mainland China. This forward-looking indicator offers a glimpse of the near-term sector outlook, and its forecast values for the current month and the next two months are publicly accessible.
Wisers-HKUST Tourism Index
Project Showcase: FinSent Tracking Sentiment of Listed Companies’ Disclosure
Developed by CBSA, FinSent is an interactive financial sentiment analysis web portal that automatically tracks sentiment expressed in financial reports and earnings calls of companies listed in Hong Kong and the United States. Using leading-edge Natural Language Processing (NLP) technology, this free-to-use service offers investors a fresh perspective for analyzing listed companies. It goes beyond financial information by classifying the underlying sentiments in company filings or earnings calls and presenting them in a user-friendly format.
For each enquiry, FinSent returns the percentages of positive, negative and neutral sentiments, showing changes of sentiments over time. Users can also compare net sentiment percentages between companies. FinSent is backed by FinBERT, a state-of-the-art deep-learning-based NLP model developed by HKUST for analyzing financial data. FinBERT outperforms Google’s BERT model in accuracy when classifying sentiments in financial text.
Investment industry practitioners from the major investment firms and financial institutions who have used the FinBERT model found it very accurate in classifying sentiments in their proprietary dataset.
The FinSent project is led by Professor Allen HUANG, Associate Dean of HKUST Business School and CBSA Associate Director (left) and Associate Professor YANG Yi of the Department of Information Systems, Business Statistics and Operations Management.
FinSent financial sentiment analysis web portal