In today’s fast-paced world, a person’s opinion about a product, service or individual is unpredictable and changing constantly, yet companies can use this unpredictability for their own benefit.
Sentiment analysis is an extremely useful tool to monitor the public opinion of certain topics across social media, like stock market trends or political campaign announcements. By analyzing text—such as posts and reviews uploaded by users on different platforms—sentiment analysis helps businesses understand the social sentiment of a brand, product or service.
Sentiment analysis tools work by analyzing an incoming message that dictates whether the underlying sentiment is positive, negative or neutral. As an example, you can play around with this demo by Parallel Dots to see how it works.
Sentiment analysis approaches are divided into two major categories: machine learning and the lexicon-based approach.
Recent advances in machine learning have considerably improved any algorithm’s ability to analyze text. Algorithm-based sentiment analysis tools can handle huge volumes of customer feedback consistently and accurately. By quickly processing people’s feelings that are shared on the internet, companies have the power to predict economy and social trends. The goal for most companies is to create strategies according to the analyzed information provided by sentiment analysis.
The second approach is the lexicon-based method. This method uses text analytics to unlock hidden meaning behind text written by, or about, customers. With this approach, it is necessary to create a dictionary of positive and negative words, with a positive or negative sentiment value assigned to each. The goal is to uncover patterns and themes regarding customer thoughts, desires and needs.
Companies can implement sentiment analysis tools across a wide range of spaces and situations, with social media providing the most powerful source of information. Regardless, the amount of information is usually gigantic and confusing. Companies have to monitor a diverse variety of social media channels: Therefore, it is important for them to target those channels utilized the most by audiences if they want to find any relevant information.
For example, the crypto community utilizes very specific channels such as Reddit, Steemit and Telegram. Cryptocurrency and blockchain investments create conversations. They create excitement, opinions, beliefs, biases and, most of all, emotion. As a result, sentiment analysis tools could provide useful insights into the crypto market sentiment. These conversations and speculations often have the most effect on cryptocurrency and ICO (initial coin offering) value.
Twitter is useful when determining the overall opinion of a particular trending topic, and other social media channels such as Pinterest or Tumblr are great when targeting very specific audiences such as millennials and moms.
Other specific areas, such as finance, can use sentiment analysis to monitor financial news, specifically to predict the behavior and possible trends of the stock market.
Furthermore, political candidates can monitor overall opinions regarding policy changes, as well as campaign announcements, enabling them to tweak their messaging to better relate to voters. For example, while Hillary Clinton was pitted as a favorite by news media outlets during the run-up to the 2016 U.S. election, a closer look at the public sentiment on Twitter and Facebook during the cycle of presidential debates highlighted otherwise. By reading sentiment data across these platforms, there was a clear indication that Clinton was in fact not clicking so well with her voters in the same way that Donald Trump was resonating with his. While Trump’s win came as a shock to many due to unfavorable articles that were being spit out by news outlets, a closer look at public sentiment against Clinton makes Trump’s win much less shocking.
In a review-based society that places value on a person’s opinion, sentiment analysis tools allow companies to achieve meaningful insights provided by comments on the web, news and on social media. Machine learning can analyze high amounts of data collected from the web in a very short time, while lexicon-based methods unlock underlying meaning from texts. As a result, companies can use this information to analyze product reviews, predict trends and new announcements, and to create and strategize accordingly.
Elliot Rothfield is co-founder and director of sentiment analysis application WatermelonBlock.