A Study: The Science of Online Sentiment Tied to Emotion as Brand Drivers

Effective online marketing campaigns carry deep emotional queue’s that connect consumers to the brand and build loyalty. Many marketers are focused on like, dislike or neutral, when there is a pot of rich data to be discovered! Utilizing sentiment is only one part of the analysis to understand online reactions and how to respond effectively and build a relationship or halt an issue. One must dive deeper to understand the implications for each post response.

 
For example, a post that says, “I just love when my dream school denies my application” gets classified as “love”, however, in the context of this statement, the word is used to convey frustration from the overall experience, hence resulting in a negative sentiment. But, let’s take this one step further and tie in the second part of the analysis- emotions.  As shown below in Robert Plutchick’s wheel of emotions, there are 8 emotions we can use to test general sentiment against and gain a deeper understanding of a consumer’s response:

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These emotional classifications, on the other hand, use natural language  and linguistics when assigning various emotions (e.g., anger, excitement, love, irritation, hope) to online responses, and therefore can have a “measure the intensity” regarding the emotions articulated. If we revisit the example from above, we first analyze the sentiment, and realize we cannot classify the word “love” as “positive”. The context of the statement positions it as negative. The next step is to determine where along the eight primary emotions, “negative” would reflect the response within the context.  A few emotions that could be categorized are disgust and annoyance.  Based on this, the response needs to consider these factors, acknowledge the frustration and attempt to deliver a solution of hope for the future.

All of this leads to my current presentation and work within online sentiment and true emotional understanding which can maximize a brand’s efforts.  In addition, my work within this area continues to grow, as I analyze generational cohorts and differences within linguistics among millennial and Generation X.