The digital age is characterised increasingly by the collective. The information generated by tapping into the minds of many is driving decisions in both the public and private sector; research is becoming social.
On the back of this, a new science has emerged – known as opinion mining – which uses the latest advances in artificial intelligence (AI) to mine public opinion for sentiment. This structured data is known as opinion data. By analysing online public opinion, governments and global organisations can now access a set of insights that can shape strategy and better measure the public’s experience of their policies, service and brands.
Crucially, the field of opinion mining looks not only at sentiment, but the topics driving that sentiment. This has also opened up new research capabilities in the world of market and political research. The sheer quantity of online conversations (Facebook alone had 1.15 billion mobile daily active users on average for December 2016), coupled with the instantaneous reactivity of this digital chatter, has meant that sentiment-driven opinion data has become a mineable, monetisable resource.
Approaches to opinion mining
There are two main approaches to opinion mining. The first exclusively uses AI to structure the data. The second adds an extra layer of analysis by processing the data through a crowd – a team of people that verify the data for sentiment and the topics driving the sentiment. The crowd’s role is to help correctly classify the unstructured data, as pure AI based approaches struggle with the nuances of human conversation. This problem is especially common in social media where conversations are typically filled with humour, slang, innuendo, sarcasm, colloquialisms and emoticons.
For example, a tweet from someone saying “I just spent 5 hours in the queue at bank X… best customer service ever!” is clearly sarcastic, but to a machine will likely be seen as positive.
By combining AI and human understanding you get the best of both worlds – the ability to gather and process huge data sets and still gain an accurate understanding of the public’s feelings.
The changing of the guard
Historically, such data was used primarily to guide commercial decision-making – largely by providing companies with deeper insight into the consumer experience.
A much wider ambit is possible, however. While part remains firmly within the commercial realm, an increasing proportion lies in the governmental/state/city arena. In the latter, social analytics are proving useful in both deepening the understanding of electorates and driving strategic political decisions.
New markets and prospects
Agility, reactivity, and leanness – these are among the buzzwords for businesses operating within the current landscape of changing customer relationships, increasing competition and the threat of disruptive innovation. Moreover, with growth having stalled in much of the developed world, many multinationals are looking for data that can help them move into new untested markets.
With the global adoption of mobile phones and the concurrent explosion in the use of social media, companies now have access to millions of relevant data points in both mature and developing markets. The data, if mined correctly, can provide Go to the full article.
Source:: Business 2 Community