Sentiment Analysis (some call it Opinion Mining) means mining unstructured data – usually through Natural Language Processing (NLP) or other text analytics technology – not just for what people are talking about, but also for how they feel about it, and then aggregating the data to find patterns that provide insight as to why they feel that way or how different sub segments respond differently.
Sentiment is what makes insights actionable. You might ask sentiment-related questions on a survey, but even if you are able to predict the full and accurate range of sentiment about a particular topic, you can’t know the why behind those feelings. And if you don’t know the why, you can’t address it, fix it, build upon it, or capitalize on it. The best you can do is know it, and that is not actionable.
Sentiment Analysis requires a fair amount of data, the right tools to allow you to analyze it efficiently and effectively, and the technical capabilities to allow you to compare results in context so that the insights become truly actionable. Here are the basics:
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