donderdag 22 mei 2014

The Last Mile in the Belgian Elections (V)

Why Sentiment Measures Alone Are Not Enough


In the process of developing Social Analytics and Monitoring, we learnt something most interesting about sentiment analysis. Before we created Data2Action  as a platform for data mining and developed SAM (Social Analytics and Monitoring) we studied many approaches.
Many of these were just producing numbers to express sentiment versus a brand, a person, a concept or a company, to name a few.
Isolated Sentiment Analysis is Meaningless
This can be too superficial to produce meaningful analytic results so we recreated social constructs that match with concepts. Analysing the sentiment of a construct element in context with a topic is not a trivial task. But at least it approaches human judgement and it can be trained to increase precision and relevance.
Today, I am not going to amaze you with Big Numbers but I’ll show you some examples of how we approach sentiment analysis with SAM.
Let’s take a few tweets about the N-VA party and examine how they are scored:
The ultimate horror for companies and a torpedo for our welfare state: an anti N-VA coalition with the ecologist party
Another point where N-VA does not represent the Flemish people
From a one-dimensional point of view, both tweets are negative for N-VA but the first is in fact meant as a positive, pro N-VA statement.
Let us look at this, more complex tweet:
Vande Lanotte opens up the coalition for the Green Party, wrong move as the voters already consider N-VA strong enough.
The first part of the sentence “Vande Lanotte opens up the coalition for the Green Party” can be considered positive for Vande Lanotte and his socialist party SP-A. But the second part is negative. This shows the importance of parsing the sentence correctly and attributing scores as a function of viewpoints.



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