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Sentiment Analysis for Brand Management

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Sentiment Analysis for Brand Management

Used well, sentiment analysis can enable you to maneuver yourself into prime popularity. (Sound familiar? #FakeNews.)

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Sentiment analysis (or “emotion mining,” if you prefer) is a really exciting area of research for machine learning (ML) and natural language processing (NLP) applications. On the one hand, it’s exciting just because it’s an example of how mind-blowingly amazing it is what can be accomplished with machine learning and natural language processing, but it’s particularly exciting for those who stand to benefit from being able to take the temperature of public sentiment in real time — and as you can imagine, that’s a lot of people!

Understanding Unsupervised Sentiment Analysis

Let’s start at the beginning, with something called an unsupervised sentiment neuron. "Neuron" is the term used for the fundamental processing element of a neural network, either in the biological brain or in an artificial neural network. Basically, the neuron takes inputs and feeds them through a transfer function based on their connection weight, and then generates an output. (You can read more about artificial neural networks technology on the University of Toronto website.) Whereas the term “unsupervised” just means that the neuron is able to accomplish its task on its own without periodic human input. As is explained in the International Journal of Computer Applications Technology and Research, sentiment analysis research has been around since the early 2000s, but the more complex and nuanced work has always required recurring calibration with human evaluation (“supervision”) of text samples, alongside the machine learning algorithms and large data training sets. As one would expect, the goal of researchers has always been to achieve more nuanced interpretations with less human time and effort.

Researchers Anuj Sharma and Shubhamoy Dey explain that, in the past, the machine learning approaches that showed the best accuracy in classifying text required supervision and had varying results depending on the selected features, the domain of the training data, and its quality and quantity. A recent discovery by OpenAI, however, may have altered the playing field for good. Earlier this year, they were processing large amounts of data through recurrent neural networks to train large unsupervised next-step-prediction models. In the process, they made the unexpected discovery that this technique could create systems with good representation learning capabilities. In their blog, they recall that

“We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment. We believe the phenomenon is not specific to our model, but is instead a general property of certain large neural networks that are trained to predict the next step or dimension in their inputs.”

Another factor contributing to the ongoing advancement of emotion mining is the easy access researchers have to large datasets made possible in the Web 2.0 era. The prevalence of social media posts and online reviews and forums provide researchers with massive amounts of data (text samples) that they can use to train and test their artificial neural networks. Combine that with OpenAI’s significant discovery and it’s very likely we will begin to see better and better sentiment analysis that is more nuanced and accurate while being cheaper and easier for all to access. This is great news for any entity for whom public sentiment is important. In simple terms, accessing this kind of information allows you to create a feedback loop with your target audience in something close to real time. You need to know how people are responding to what you’re putting out there (whether that be a message, a product, a branding effort, or other) in order to respond accordingly. Used well, sentiment analysis can enable you to maneuver yourself into prime popularity. (Sound familiar? #FakeNews.)

Using Sentiment Analysis to Empower Messaging

What your success looks like depends on what you’re “selling.” Think of a politician, for example. Sentiment analysis can tell you if your policy idea is popular or what style of communication gets the most positive response. Simply put, it’s a little like performing A/B tests to detect emotional responses. Non-profits and charities strategically structure their campaigns around sophisticated sentiment analysis data; not to mention, they rely on these emotional responses to raise awareness and mobilize popular sentiment around certain issues. Think of the brilliant two-minute Sick Kids ad campaign created by Cossette where children in hospitals are dressed as superheroes as they fight disease and combat serious illnesses. The imagery, the music, and the message were flawlessly framed to evoke a profound and meaningful emotional response; one that leaves the viewer hopeful, yet compelled to take action. This is just one of the many examples of how sentiment analysis can be used to empower brands and their messaging.

Another example is how financial experts use sentiment analysis to gauge markets and guide their investing. The uses are endless for companies as well, both for brand management and PR, as well as for selling products. It’s not too far a stretch to imagine that, in the near future, it will be possible for brands to harness the power of sentiment analysis to predict emotional responses. Consequently, with the power to predict comes the ability to influence. Just imagine what this means in when zero interface conversational environments become more prevalent. Could this be the brave new world of client relationships and user experience? How can we ensure that this data is ethically extracted and analyzed?

On that note, I ask you to reflect on how your business and brand could benefit from a nuanced, real-time understanding of the public sentiment? What would you do if you had access to this information at your fingertips, or how would you respond? Or, do you foresee any ways in which this technique could be hijacked for malicious purposes? If so, how can we prevent this from happening?

Think about it and let those ideas sit for a while. When you’re ready, let me know your thoughts in the comments below or send me tweet! Thanks a lot for reading. Cheers!

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Topics:
ai ,machine learning ,sentiment analysis ,nlp ,ai apps ,emotion mining

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