Take a look at the state of sentiment analysis and explore future directions.
Focused or integrated. Do you need to go beyond simple figures to get at breakdowns, root causes, and predictions?
Integrated analyses seek to link sentiment to psychological profile, behaviors, demographic characteristics, transactions, events, and/or other data.
How It's Done. Here are five different functional sentiment analysis techniques:
1) Analysis by trained analysts.
2) Crowd-sourced analysis by untrained humans.
3) Automated analysis of information extracted from unstructured sources such as text, audio, images, and video--for instance, for text via natural-language processing (NLP).
4) Analysis of categorical poll or survey questions--for example, "Rate your hotel stay on a scale of 1 to 5," or the equivalent star ratings.
5) Inference of sentiment from numerical statistics--for instance, commercial inventories, consumer spending, investment levels, etc. Automated NLP may apply linguistic, statistical, and/or machine-learning techniques.
ROI. There's sentiment analysis that delivers business value, and there's eye candy. We see lots of social media analytics and BI dashboards that convey sentiment via pie charts, trend lines, color-coded word clouds, and other graphics. With most of these, I've concluded, you face a decision gap: They give you information (sometimes accurate, sometimes not), but they don't convey meaning or suggest how you can use the information to improve your business decision-making.
The type of sentiment analysis that will work for you is analysis that is aligned to your business goals. Work back from your goals to understand the type of insights that will best help you make better business decisions. Decide what data sources you need to tap and which analytical techniques and level of granularity are appropriate. Determine exactly what you're going to measure and how you're going to link sentiment to the wealth of other types of data available. Then you'll be positioned for return on investment--the only type of sentiment analysis that really matters: sentiment analysis that delivers business value.
Let's conclude with a look ahead. Sentiment analysis is evolving in the following important directions:
--As an industry. Adoption of sentiment analysis is growing in a spectrum of business domains and applications. The anti-automation backlash continues, but it should fade as sentiment analysis providers and users move toward semantically infused analysis with feature-level, business-need-aligned sentiment resolution and away from simplistic, keyword-based solutions.
--Considering sources. Detection and exploitation of emotion in speech and images (such as facial and body language) implied by video-captured behaviors will increasingly come into play, including actively in meeting commerce and security needs. Availability of solutions for smaller-market languages will remain demand-driven.
--A focus on intent. We seek to understand not just how people feel, but what their feelings, linked to data from a variety of relevant associated sources, say about plans.
--Predictive modeling. Sentiment analysis becomes fuel for efforts to shape opinion, attitude, and emotion.
The end result is sentiment analysis as a contributor to sense-making, to intelligent automation that enables machines to understand and act on the spectrum of signals present in the human world.
Seth Grimes is an analytics industry observer--an analyst, consultant, and writer--who helps organizations find business value in enterprise data and online information. Seth consults via Alta Plana Corporation, works as an industry analyst, organizes the Sentiment Analysis Symposium, and tweets at @sethgrimes.
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