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What is Sentiment Analysis?

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.

Why is Sentiment Analysis Important?

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.

How to Analyze Sentiment from Unstructured Data

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:

  1. Collect Data from your Customers, Employees or other Constituents

  2. Mine and Classify the Data at the Sentence, Phrase and Concept level

    • This step requires more than a good pair of reading glasses.  Use a text analytics tool like Natural Language Processing (NLP), Machine Learning, or Semi-Automated technology.  Which technology is best for you depends on the volume, complexity and maturity of your unstructured data. Find a provider/partner that can help you assess and provide the optimal set of tools for you.
  3. Apply Data Visualization, Filters and Cross Tabulation

    • It’s not enough to just put the sentiments into buckets.  To gain actionable insights, you need to look at differences in sentiment by factors like customer segment, population, or geography, and quickly pinpoint areas of greater concern or opportunity based on trends in the data and helpful data visualization. The right text analytic tools will give you user-friendly ways to uncover and share the key insights in your data.

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