Symptoms are always there, but it takes a doctor to ‘connect the symptoms together’ and diagnose the illness. Pieces of evidence are always there, but it takes a Sherlock to ‘process all that information together’ and solve the puzzle. These, among many others, coincide with one important point – ‘information is vital, but it needs intelligence to make sense out of it’.
Today, technology has pushed the envelope to an extent that humans are the core beneficiaries of the ‘human-like’ intelligence being culled out from computers. There are names to it like – artificial intelligence, machine learning, big data analytics, predictive intelligence, and much more. Multiple unprecedented problems are being solved today – be it health, financial matters, businesses, social analytics, or even political happenings and election campaigns.
To no surprise, big data analytics and elections are closely related as it is redefining the ways election campaigns are designed is just one aspect of the story. Over and beyond, this exact attribute of data – of being able to radiate sense out of it – is today helping in continuously calibrating the campaign planning and execution in real-time. Here are a few ambits of data-driven and machine-based intelligence that astonishingly bear out the vitality of data.
Social Engagement Analytics
This goes from analyzing the latest trends across social media and search engines to performing the Sentiment Analysis of what people are talking about and about whom. For example, Engagement analysis for Twitter and Facebook and analyzing the sentiments of the massive amount of mass-generated social media data.
For instance, the below sample image draws inferences about countries from where each of the candidates are getting the most tweets in the X-Y period. It also depicts a sentiment analysis on the Twitter mentions of the Candidates.
Natural Language Processing
It’s indeed implausible for a human to guzzle up hundreds of interviews and related articles and enlist and a set of cognitive and informative inferences. Of course, machines when to take a human role can do exactly that within an incredibly short span of time. Analyzing both speech-based interviews, as well as text-based transcripts, can help derive aspects such as:
- Speech complexity
- Mentions of election agenda, other countries, themes, etc.
- Word analysis
Flesch Reading Ease Index is one of the ways that help in understanding speech complexity and the level of speeches – which in turn, ensures that the speeches are more populist and are apt in reaching the wider masses.
Rendering real-time N-grams, word clouds, time-scaled trends, and others – are some very popular ways of culling out ‘related’ or ‘closest-in-the-vicinity’ words which helps the political analyst derive the right inferences.
# Speech Analytics
There are platforms and solutions that can trawl through the election-related media coverages and interviews and generate a thorough analysis of what are the key election topics, which candidate is harping on what themes, alignment, and mapping to the manifesto and what not.
For instance, the below sample image infers that - while Democrats have been more worried about the social and economic factors, republicans have been more worried about the relations of the US with the world and foreign policy.
Adding the geography dimensions to the sentiment analysis or other social media attributes (mentions, hashtags, etc.) hands over a strong contrivance to derive the public sentiments in different regions or geographies. For instance, here’s a sample image of 2020 Presidential Election projections
Data without the means to draw on insightful meaning is no more than a white elephant. It stands to reason that electoral parties today do not leave any stones unturned when it comes to utilizing data and being responsive to the inferences that are derived.
As simple as it may look, the strength of big data analytics can be easily summarized as – information begets insights, insights beget knowledge and knowledge begets actions!