With the increased penetration of internet and mobile, user-generated content is growing at a rapid pace. Also with a cut-throat race to acquire a customer or even to retain a customer, every brand or company needs to understand what their customers are saying. People write their reviews on various websites, facebook, tweet about it or post photos with comments. If brands or companies want to understand if their brand is being talked about in a positive way or a negative way, what they need is to carry out Sentiment Analysis on this data.
But, what is Sentiment Analysis?
As the name suggests, it is the analysis of data to find out what that data is representing. Are there more happy emotions or sad emotions or there is anger. The Sentiment Analysis tools capture data from various sources. Various types of algorithms are run on this data to identify the emotions appearing in the data. Natural Language Processing (NLP) and Machine Learning (ML) are important backbones of this analysis. NLP allows the tool to process human language. ML allows the tool to learn various moods appearing in the data.
Challenges
Humans have weird ways to express themselves. When someone says “Wow!!”, it could mean real appreciation or it could mean sarcasm. We also say “Hating <brand> is not really my thing” – which may be a positive comment about the brand. Or when we say “He was so aggressive, but then I used to like him” – it represents mixed emotions. “I really love my phone, and I’d hate to lose it” – two different emotions about two different entities. And I can go on and on. Hence the tool has to learn all such variations and then come up with a score which would help the brands and businesses to improve their services. Or such score could also be used to create a marketing plan around the emotions.
The tool analyses words, context, the frequency of words, their occurrences with other words and then starts giving you insights. Take a look at the data gathered from tweets during Chennai Rains in 2015.
You can read the full analysis and see how sentiment analysis could be useful even during crisis situations. Or take a look at data that was analyzed on World Book Day about two biggest e-commerce players (then!!). As you would see, the analysis suggests a marketing plan based on the Sentiment Analysis.
Related Links
Related Keywords
Natural Language Processing (NLP), Machine Learning, Supervised Machine Learning