Using Social Media Data for Comparing Brand Awareness, Levels of Consumer Engagement, Public Opinion and Sentiment for Big Four Australian Banks

Kolyshkina, I., Levin, B. and Goldsworthy, G.

The growing availability and popularity of opinion- rich resources on the web led to an eruption of activity in the area of analysis of data coming from these resources. Opportunities exist to understand the extent of public engagement and sentiment toward a brand, a product or an event. In this paper, we present a case study for opinion extraction applied to the banking domain that illustrates how social media can be used to gain insight into the public opinion, sentiment and spread of social conversation related to this domain including changes that are triggered by a domain-relevant event. We applied advanced machine learning and data science techniques to the relevant social media and news data from the web to analyse the nature of public opinion in Australia toward the four major Australian banks in the context of the banks reaction to the Reserve Bank of Australia lowering the official interest rate. The resulting insights into public sentiment, reach, the topics discussed by the public and how these compared between the banks can be used proactively to inform organisational decision making.

Cite as: Kolyshkina, I., Levin, B. and Goldsworthy, G. (2013). Using Social Media Data for Comparing Brand Awareness, Levels of Consumer Engagement, Public Opinion and Sentiment for Big Four Australian Banks. In Proc. Eleventh Australasian Data Mining Conference (AusDM13) Canberra, Australia. CRPIT, 146. Christen, P., Kennedy, P., Liu, L., Ong, K.L., Stranieri, A. and Zhao, Y. Eds., ACS. 59-63

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