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Social media has enjoyed a swift graduation from a trend to a lifestyle shift for a large chunk of the world’s population. The business community was quick to realize this transition. It didn’t take long for companies to start looking for how this change could benefit them. Soon, they were interested in knowing what people were saying about them and their competition on Twitter or Facebook. Social media became a way for companies to gauge how people felt about their brands, company, product experiences or customer service. In fact, as technologies continue to advance, such data can now be captured in real time (even at frequencies of as low as a millisecond). And all this can be done without disrupting consumers at all. In more recent years, the analysis of social media data has become known as sentiment analysis. Here we’ll take a look at how it works – and when companies should implement it.
What Is Sentiment Analysis?
Sentiment analysis is a process of systematically and programmatically extracting text information, such as tweets, statuses, comments and posts from the Web. The key here lies in analyzing these large data sets to decipher them into emotions, opinions and consumer sentiments. This information helps business decision makers evaluate how their customers feel about their brands. Such analyses can either be done on a specific segment of customers or on the whole set of customers.
How Is Sentiment Data Captured?
Back in 2010, the field of sentiment analysis was still shaping up. Back then, such analyses were based on word lists containing a set of keywords classified as either "good" or "bad." These words were assigned a predefined value based on the degree of the emotion conveyed. The tweets or posts were then checked for these keywords and, based on the level of match, the overall intent of the tweet/post was determined.
Of course, there were some obvious pitfalls in using this technique. The biggest problem with this approach was that it was vulnerable to producing inaccurate results. After all, many words can be used in different ways and have different meanings depending on their context. The systems were inept at determining the context in which the messages were framed. This rendered any such analysis useless, which was pretty clear based on the very low accuracy rates of sentiment data at the time, when less than 50 percent of the results were considered valid.
This is where human intervention becomes indispensable. So, in more recent years, some of the major sentiment analysis companies such as the FACE group and DataSift have been using a mix of manual and automatic techniques to improve sentiment data’s accuracy. A team of people manually verifies some of the results after a fixed interval to improve the system’s reliability. Even this modification does not lead to a 100 percent success rate, as each individual sees the same thing in a different context, and their knowledge and judgment of a certain subject may differ from those of experts. In addition, there’s no objective way of detecting sarcasm or inferring the tone in which messages are framed.
At this point, you may be wondering why anyone would want to monitor social media when the results are so unreliable? The answer is simple. Although sentiment analysis may not provide the most accurate picture of how your brand has fared over time, or how your latest marketing campaign was received by the target audience, it’s quite good at one thing: Detecting early warning signals.
No company wants to be bad-mouthed on social media, but if they don’t know about it, they can’t even do damage control. For example, in 2009, two employees of a Domino’s Pizza chain posted a video of themselves contaminating customer pizzas (not to mention violating health code rules) on YouTube. The video went viral, and put a major dent in the company’s reputation. If Domino’s had learned of the video before millions had seen it, they may have been better prepared to address the problems it caused for the company. (Get more tips in Twitter Fail: 15 Things You Should Never Do On Twitter.)
But Before You Adopt a Sentiment Analysis Strategy …
Sentiment analysis has its benefits, but there are big challenges too. Here are some questions enterprises should ask before they start collecting social media data.
Which Channel to Monitor?
One of the major challenges in terms of monitoring social media is to decide which social media channel to tap into. Twitter, Facebook, LinkedIn, blogs, e-commerce sites (product reviews) and news sites are the most popular choices. Determining which ones to focus on will depend on the company’s target market.
What Do You Plan to Learn?
Although the fancy UIs offered by some applications give a good impression of being robust, they should also be capable of delivering actionable insights within a reasonable time frame. If you don’t have those, you don’t have a sentiment analysis strategy.
Who’s Accountable?
Someone within the organization must be entrusted with the task of monitoring and controlling each social media channel. Guidelines must be established regarding how common concerns should be addressed. If this framework isn’t in place, sentiment analysis isn’t likely to deliver much value.
If a company is looking to analyze only selective channels, it may not result in large quantities of data. Such companies can consider hiring a service provider on a contractual basis. Doing this is more cost-effective than buying an analytics application and customizing it to suit specific needs. This approach may also lead to shorter turnaround times.
Social media monitoring has come a long way and is delivering real benefits, at least for those companies who manage the process effectively and efficiently. But while in the past, decision makers had to ask themselves whether monitoring social media would add value to their business, the real question has now become exactly how it will affect revenue.
