They don’t call them social media for nothing.

They don’t call them social media for nothing.

Emotions play a huge role in effective marketing communications.

None of us acts or reacts on a purely rational basis, no matter how cold and calculating we may be. And, social media – the wellspring of user-generated content – should be richly emotional. After all, if condensing moments of our lives into 140 characters or less and broadcasting them to legions of followers doesn’t call for a little emotion, what will?

Why should this matter to business? First, as most researchers will point out, there are a wide range of emotions that lie between “Good” and “Bad.” Companies need to know the intensity with which an opinion is held. Monitoring blogs, Facebook posts and Twitter followers may provide a sense of overall opinions and attitudes, but it’s important to know how deeply customers, prospects, or former customers hold an opinion. A new product or service may create overall satisfaction, but that shouldn’t be confused with outright delight. Which means those positive comments you’re hearing may not be strong enough to support the sales gains you’re projecting.

Here are a few tools that might help you in monitoring emotions via social media:


This free analysis tool for Twitter evaluates the general mood of a tweet. TweetFeel focuses primarily on distinct words like love, hate, better, etc. Search results flow down the screen, calculated as positive or negative words. The service also offers an overall percentage number to indicate whether the majority of results are one or the other. TweetFeel can be used to find out about the emotions users are feeling towards a brand, product, service, company and so on.

This company also offers a service called TweetFeel Biz, which uses more complex rules and algorithms for analyzing and trending data over weeks and months. It allows businesses to track, trend, compare and create stories about their brands. If you’re interested, please visit: /


Used to analyze a website or text based on its general polarity, ContextSense’s results are displayed as a percentage between 0 (negative) and 1 (positive). ContextSense can predict conversions on a website using visitor behavior. They begin with the basic premise that the base probability of contacting is around 4.2% and every minute spent by the visitor on the ContextSense page increases the probability of contacting by 1.8%. This, of course, assumes that the interest of a visitor increases linearly with time (when in fact it may be exponential).
The most important predictor for detecting if a visitor will visit the Contact Us page turns out to be number of pages viewed by him or her. So, to get visitors interested enough to visit the Contact Us page, it is vital to have an engaging website where visitors can fully explore what you have to offer. For actual conversions (people who fill the “contact us” form and get in touch for more information), the time spent on the landing page turns out to be most important predictor. This shows that the landing page must match with the intent of the visitor.


Looking to expand your basic search engine capability with additional emotionally charged adjectives? RankSpeed users, for example, can search for smart phones that are excellent, useful or popular. RankSpeed analyzes expressions on blogs and Twitter and enables conventional web searches as well as a specific product search. RankSpeed offers an inspirational taste of the possibilities of a progressive technology called Sentiment Analysis. You can find more information at:

Sentiment Analysis

Social networks can be searched for the sentiments and emotions of the users using Sentiment Analysis. This technology scans the web for keywords analyzing polarity, subjectivity and the kinds of word choices people are using. Using these data and with the aid of algorithms, information about emotions that are expressed in text can be provided. Still in the infant stage, Sentiment Analysis is being used by some well-known social media players.

Sentiment Analysis will no doubt evolve and mature in the near term. Among the basic requirements for the area to be successful is the development of a very extensive database of words and languages, different abbreviations, vernacular and – of course – the constantly changing slang. In addition, there is a need for semantic technologies to understand overall context.

As all these requirements are met, companies will be increasingly able to identify and cluster brand, product and service attitudes based on the intensity of emotion. Strongly positive or negative comments point to severe issues that demand immediate intervention; whereas, tepid positive or negative attitudes might suggest a weakness in future re-purchase intentions or overall loyalty. Regardless, it appears we are moving from simply knowing what people are thinking to a more granular scale of how passionately they’re thinking it.

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