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.
