Social sentiment analysis today
Sometimes called opinion mining, sentiment analysis is the holy grail – the most effective way to find out what consumers think about a brand, product or event, in real time. It aims systematically to identify opinions in a document and give it a score on a scale of negative to positive. Marketers use instant sentiment data to make campaign adjustments in close to real-time, instantly feeding negative reviews to product teams and solving major customer issues more efficiently.There are two main approaches in use today: sentiment analysis based on keyword scoring, and a calculation based on predefined categories.
Keyword scoring means you give the word ‘good’ a positive score, the word ‘bad’ a negative score. This approach has inherent flaws which some analysts try to fix by applying rules on top of the keyword scoring, but the underlying problem remains: keyword scoring only captures fragments of a message and struggles to accurately judge true meaning. Keyword scoring can deliver some relevant results, if focused on broad trends or applied at scale. But, at a more detailed level, a sarcastic tweet or a 13-year-old calling a new video game ‘sick’ brings the system to its knees. Because keyword-based sentiment technology can’t understand context – only individual words or small phrases – proven accuracy levels generally range between 50 to 80%. This has led to many brands sidelining sentiment data in reports.
The second approach is based on the idea that you let the user categorise a few dozen results as a training set, and then let an algorithm use this to make decisions for future results. Accuracy for predefined categories is usually higher than for keyword scoring, but it’s far from perfect. This manual categorisation requires a huge time investment and an understanding of all topics that could potentially be linked to your brand. Because this method works with such narrow parameters for qualifying results, it typically yields fewer results overall.
In a third approach, some analysts prefer to bypass technology altogether and require people to code sentiment. The upside here is that accuracy is no longer a problem. People will disagree here and there (language is not an exact science), but overall results are excellent. The downside? On average, a person can only classify about 100 documents per hour. Brands can get thousands, even tens of thousands, of mentions a day.
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