Sentiment analysis: Mining the hearts and minds


It is becoming more and more apparent that social media is a gold mine of unstructured data that is just waiting to be analysed so that the nuggets can be extracted. At SAS Global Forum, I was particularly impressed with the diversified use of sentiment analysis and the exploration that has been conducted into the field of social media. I attended a number of great presentations and an extremely interesting Super Demo on the analysis of consumers’ moods during Super Bowl commercials.

Analyzing passion

The Super Demo detailed how to use mood statements alongside sentiment analysis to measure in more detail the emotion displayed by people - more than would be possible with sentiment analysis alone. For example, the underlying purpose of advertising is to generate a reaction, hopefully positive, to a particular product or service. The key, therefore, is to understand this reaction through the use of social media to determine the best marketing strategies to implement.

Text analytics can be used here to derive the emotions people are displaying through the words and phrases they use on social networking sites such as Twitter and Facebook. From this data, sentiment and intensity (defined here as the “passion” component) can be derived to determine which commercials hit the mark with their targeted audience. Read this blog post by Richard Foley about analyzing sentiment for more information about the Superbowl research.

Predicting outcomes

Another thought-provoking presentation on a novel implementation of sentiment analysis and forecasting was given on the topic of predicting electoral outcomes. The purpose of this presentation and paper was to try to predict the outcomes of popular elections through social media when polling data is not necessarily available. It also demonstrated the ability to validate election outcomes and check for potential instances of fraudulent election administration.

What was interesting (maybe more than the demonstration on popular elections) was the demonstration of this same methodology on the popular television show American Idol!

The four-step methodology given to achieve this through the extraction, validation, analysis, and prediction of outcomes from the relevant social media data was:

  1. Extract a set of Tweets about the candidate of interest.
  2. Filter the Tweets to ensure that the keyword pulls are relevant.
  3. Analyse the Tweets for positive or negative sentiment around a candidate using sentiment analysis.
  4. Predict contest winners based on the aggregate sentiment scores for the candidate of interest over time using forecasting.

This process allows researchers to surface the general opinions of the social sphere at differing time points to determine a view of sentiment before and after a particular event, for example an eviction from the show.

Not only is sentiment analysis crucial for this exploration, but there are also forecasting applications to determine future events given the textual information that has been determined from the sentiment analysis. Check out Jenn Sykes’ full paper, Predicting Electoral Outcomes with SAS ® Sentiment Analysis and SAS ® Forecast Studio. Also take a minute to watch her in this short Inside SAS Global Forum interview.

With regards to the application of sentiment analysis in other sectors, I can see that there is certainly potential here in the financial sector, where there is a great need for information on sentiment from customers, not only for marketing-related activities, but also customer retention and acquisition.

This year’s conference was a fantastic display of what to look forward to in the world of analytics, and the next SAS Global Forum, San Francisco April 28th thru May 1st is already in the diary!


About Author

Iain Brown

Head of Data Science SAS UK&I / Adjunct Professor of Marketing Analytics

Dr. Iain Brown (Twitter: @IainLJBrown) is the Head of Data Science at SAS and Adjunct Professor of Marketing Analytics at University of Southampton working across the Financial Services sector, providing thought leadership in Risk, AI and Machine Learning. Prior to joining SAS, Iain worked for one of the largest UK retail banks in the Risk department.

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