In simple words, sentiment analysis is nothing but a fancy term for opinions, it is the analysis of feelings, attitudes, and opinions. It is a systematic way of reading between the lines. Opinions are mined behind specific words, using tools like Natural Language Processing (NLP). So essentially not only are we looking at your likes and comments but are making a systematic effort in ‘Understanding’ your reaction, like does a certain behaviour look sarcastic? Or Negative? Or is it indeed positive?
Being able to accurately capture this information is very vital for marketers, as they get an accurate response to their social content, and estimate if it is hitting the accurate target or getting the correct response, having access to this information can help us course-correct our interventions to resonate on the targeted touch points, with our end users. Unlike measuring quantifiable metrics, sentiment analysis measure what matters.
It measures quality metrics like opinions, feelings, satisfaction ratings and most importantly ‘Quality of Engagement’ over time. So say I viewed a particular add but was I stationary or toggling between screens? And if toggling, at what points? Such feedback is priceless.
There are many monitoring tools which can be used in combination or individually to analyse user sentiment.
At Imarticus we offer a wide range of hybrid courses, with online and classroom engagement. While offering online courses we have to be very conscious especially while designing the course content, as the social and online learning environment is very different from the traditional ones.
In an online course, the interaction is only possible via webinars, discussions, hence it is driven by online participation. This can be both positive and negative. Hence in some way, as providers if we can increase online participation, it will have a direct impact on overall satisfaction, the results will be better participation, improved return on investment, better retention, and overall satisfaction and reduce the number of midterm dropouts.
There is a lot of data that gets collected with the online activity of the participants, hence it directly gives us the option of analysing online engagement, this trend is gaining popularity especially for online educational courses.
In fact, in some scenarios research has proven that with an application of techniques like ‘Learning Analytics’ and ‘Educational Data Mining’, one is able to predict participant behaviour and alert a potential dropout pattern.
Applying the capability of the monitoring tools available in sentiment analysis, one can possibly find a link between the sentiment tonality of learners using the online platform for education. These findings are never definite and can be tested with many other available metrics to reach a conclusion.
