The Future of Sentiment Analysis

Sentiment analysis is a uniquely powerful tool for businesses that are looking to measure attitudes, feelings and emotions regarding their brand. To date, the majority of sentiment analysis projects have been conducted almost exclusively by companies and brands through the use of social media data, survey responses and other hubs of user-generated content. By investigating and analyzing customer sentiments, these brands are able to get an inside look at consumer behaviors and, ultimately, better serve their audiences with the products, services and experiences they offer.

The future of sentiment analysis is going to continue to dig deeper, far past the surface of the number of likes, comments and shares, and aim to reach, and truly understand, the significance of social media interactions and what they tell us about the consumers behind the screens. This forecast also predicts broader applications for sentiment analysis – brands will continue to leverage this tool, but so will individuals in the public eye, governments, nonprofits, education centers and many other organizations.

Deeper, Broader Insights from Sentiment Analysis

Sentiment analysis is getting better because social media is increasingly more emotive and expressive. A short while ago, Facebook introduced “Reactions,” which allows its users to not just ‘Like’ content, but attach an emoticon, whether it be a heart, a shocked face, angry face, etc. To the average social media user, this is a fun, seemingly silly feature that gives him or her a little more freedom with their responses. But, to anyone looking to leverage social media data for sentiment analysis, this provides an entirely new layer of data that wasn’t available before. Every time the major social media platforms update themselves and add more features, the data behind those interactions gets broader and deeper.

Greater Personalization for Audiences

As a result of deeper and better understanding of the feelings, emotions and sentiments of a brand or organization’s key, high-value audiences, members of these audiences will increasingly receive experiences and messages that are personalized and directly related to their wants and needs. Rather than segment markets based on age, gender, income and other surface demographics, organizations can further segment based on how their audience members actually feel about the brand or how they use social media. While some people shudder at the thought of companies learning more about them, more exact targeting means that, in the near future, we will no longer be scratching our head wondering why we see advertisements for products we’d never dream of purchasing. In other words, the spray-and-pray advertising tactics are almost put to rest and there will be a time when every marketing message we see will be relevant and useful to us. Sentiment analysis is going to be a large contributing factor towards achieving this vision.

Not Just For Marketers and Brands

Again, sentiment analysis is on the verge of breaking into new areas of application. While we will likely always think of it first in terms of the traditional marketing sense, the world has already seen a few ways that sentiment analysis can be used in other areas. Social media analytics helped predict and explain the emotions of concerned parties behind Brexit and the 2016 US election, which has spurred a number of non-brand organizations to investigate how sentiment analysis can be used to predict outcomes and map out the emotional landscape of people, voters and the like. Additionally, businesses are looking at ways that sentiment analysis can be used outside of their marketing and PR departments. Sentiment analysis simply looks more popular in the future.

Algorithm-Based Sentiment Analysis Plateaus

Algorithms have long been at the foundation of most forms of analytics, including social media and sentiment analysis. With recent years bringing big leaps in machine learning and artificial intelligence, many analytics solutions are looking to these technologies to replace algorithms. Unfortunately for organizations looking to leverage sentiment analysis to measure audience emotions, machine learning isn’t yet ready to tackle the complex nuances of text and how we talk, especially on social media channels that are rife with slang, sarcasm, double meanings and misspellings. These make it difficult for artificial intelligence systems to accurately sort and classify sentiments on social media. And, with any analysis project, accuracy is crucial. It is uncertain if machine learning will progress to the point that it is capable of accurately analyzing text, or if sentiment analysis projects will have to find a new basis to avoid the current plateau of algorithms. Some social media analytics solutions have begun taking a more human approach to deciphering the often ambiguous nature of text, but this can be time consuming.

Conclusions

2017 is going to be another year that continues to drive the analytics machine forward. With more and more organizations turning to sentiment analysis to measure and predict outcomes, as well as better understand consumer behaviors, these tools are quickly building a reputation that is going to help propel it forward into the future and towards deeper and more accurate conclusions and insights.

Summary: Sentiment analysis has been an important tool for brands looking to learn more about how their customers are thinking and feeling. It is a relatively simplistic form of analytics that helps brands find key areas of weakness (negative sentiments) and strengths (positive sentiments). Moving forward, sentiment analysis is finding a place in other organizations. During Brexit and the 2016 US election, these data tools were used to measure emotions and attempt to predict the outcome of these events. This has led to non-brand organizations turning to sentiment analysis for their own needs. Additionally, the insights gained from these tools are becoming much deeper, as a result of emerging social media platforms and features.