What You Need to Know About Social Media Sentiment Analysis
Social media sells, and selling drives the internet.
The need for clear, reliable information about consumer preferences has led to increasing interest in high level analysis of online social media content.
Sentiment analysis is a new, exciting and chaotic field. Here is a look at the current state of sentiment analysis and what it means for your business.
What is Sentiment Analysis?
Sentiment analysis is a form of social listening, which sounds a bit like the NSA has taken up internet marketing.
It actually means monitoring social media posts and discussions, then figuring out how participants are reacting to a brand or event.
Sentiment analysis can improve your bottom line.
The process started getting traction in 2010 and is now booming to such an extent that it has been dignified as a field of study, not a mere marketing tool.
The field is still in its growing stages and researchers freely admit that not all aspects of it are fully understood.
The challenge is that even simple sounding human conversation has layers of meaning and nuance. So it is with sentiment analysis.
For this reason, fellow humans, not just machines, need to be part of the research process. There are a number of automated tools, but all of them have varying rates of accuracy.
Humans are essential to parsing human conversation.
How Social Media Sentiment is Measured?
At its most basic, sentiment analysis is a social media analytics tool that involves checking how many negative and positive keywords are present in a chunk of conversation. If there are more positive keywords than negative, it is considered positive content.
If there are more negative keywords, it is called negative content. But there’s a lot more to it than that, and it’s real worth is found in the details.
In-depth analysis involves finding opinions in social media content and extracting the sentiment they contain. An opinion is made up of a target, also called a topic, and a sentiment on the topic.
Using an uncomplicated example, in the content “I love Crunchy Jalapeño Cheetos,” the topic is Cheetos, and the sentiment is most definitely positive.
From there, the process of analysis just gets more complex. Good sentiment analysis includes the demographics of the participants as well as context.
What Impacts the Sentiment Analysis Accuracy?
The accuracy of automated tools depends on the algorithm that underlies it. Automated tools with the ability of a human to override its workings is the most accurate.
The plus side of marketing automated tools is that they can cover a lot of conversation quickly.
If you have one Facebook page plus a Twitter account and 100 active followers, one person can handle the analysis by reading all the posts, making a judgement call and responding.
But if you have thousands of followers, you need to automate the process as much as possible as a practical measure.
Automated tools are effective at looking for high level trends and extremes of opinion. For example, if consumers are swearing about customer service at a retail computer store, there’s a good chance opinion is negative. This is essential information for the customer service department and the quicker they get it, the better.
Automation also brings the cost of analysis down considerably.
Accuracy depends on its algorithms, and dealing with human speech and opinion, so often unpredictable, calls for an extremely complex one. These are hard to develop, and there is ample room for improvement. To be effective, automated tools go well beyond positive and negative.
They need to factor in demographics. The location, gender, age, even salary of an individual is a big part of parsing opinions.
When a human is monitoring the workings of automated tools, he can provide the grounding that current technology lacks. The built-in algorithm of a human beats that found in any automated tool.
By being able to override the automated process, the person keeping an eye on the automated tool can steer it, keeping it pointed in the right direction.
He can also quickly respond to negative or positive mentions.
How Can You Use Sentiment Analysis?
Sentiment analysis can help your business in a number of ways, whether it’s large or small, by helping you make sound decisions about products and advertising. People gather in social media to discuss your products. Eavesdropping accurately and in a timely manner helps you prosper.
Listening to customer feedback has always been important to companies that want to stay in business and expand their customer base. As the marketplace gets more complex, using human and automated methods can help in a number of ways.
- You can respond quickly to a crisis, bad experiences and problems with products or customer service. It provides a way to manage your online reputation by taking appropriate action with speed. One very practical use is with reviews. People pay attention to reviews and buy more of the products that have the most reviews. Using sentiment analysis, you are alerted to negative reviews as they happen, allowing you to respond quickly.
- You can influence the conversation when you know which way it is trending, nudging the view to make it positive.
- You can gain the trust and loyalty of customers by reacting to customer feedback in a timely manner, which tells customers you care about them.
- It helps you measure the success of a specific ad or campaign soon after it appears. If people are talking about it, you’ve made an impression. Hopefully it’s a positive one.
- It acts as an important resource for market research. Knowing that women between the ages of 18 and 24 like your new app helps you figure out how to word your ads, the graphics to use and where to place them. You can refine an ad campaign while it is still going on.
- You can get a good idea how your product stacks up against that of a competitor, in the opinion of consumers. This helps with product development as well as marketing.
- You can check your virtual popularity (think likes, retweets and favorite). Developing a bigger footprint is good for business. It’s a good metric to track on an ongoing basis.
Combine Human and Machine Learning
Using a combination of human and automated research, you can use sentiment analysis to improve your marketing. To get the most out of the process, you need to be clear about what you want to accomplish, what info you are specifically looking for and the exact ways to go about it.
Decide if your business needs this tool. It may not be the best decision for you right now. If your online following is very small and you don’t yet have much online brand recognition, this method of research may not be a good investment of your time and money.
If you decide to do sentiment analysis, be very clear about your key performance indicators, or KPIs. Unless you know what you’re looking for, you’ll never know if you’ve hit your goals. The more precise you are about indicators for sentiment in the content of specific posts and campaigns, about brands and mentions, the better and more useful the data you end up with.
Managing your reputation is a worthy goal, but posting responses to negative comments will only go so far to resolving problems. Make sure the people who are the source of the problem get the message.
Support them in developing ways to prevent it from occurring.
Sensitivity Analysis is a Powerful Tool
Be careful in your use of comparisons. Though on the surface it may look like consumers are saying they like one product over another, you need to analyze the context. The better and earlier you sort out what they actually mean, the more useful the data will be to the analyzers of your data.
Realize this is complex. This is not a simple matter of more positive words showing up in comments than negative words. For example, the context they were part of and demographics of the participants has a strong influence on actual meaning.
Keep testing, and keep refining your methods of testing and of analyzing. With time and experience, you’ll figure out what consumers are really saying and the best ways to monitor them.
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