Social Data Analysis: Analyze More Than Just Metrics

Social data analysis is the collection and interpretation of social media data, whether analyzing Twitter conversations, Facebook posts, Instagram Stories, and more. It often involves identifying emotions and trending topics, and understanding how people interact with content.

There are many social media analytics tools out there that help you gather social metrics, like average reach, total engagements, total impressions, and more. And some more advanced tools can even help you track sentiment levels about a particular topic. 

However, businesses receive a huge amount of unstructured data, in other words, data that’s hard to quantify because it comes in strings of text. In some social media analytics tools, you can easily filter by topic and sentiment to get a bird's eye view of how customers perceive particular products or services, but they don't delve deep into the ‘why’. 

For example, why are there more negative opinions about customer support than usual?

That’s where social data analysis tools for text come in handy. In this article, we’ll focus on analyzing text data in social media, and why it’s important for gaining even deeper insights about your customers and your overall business.

Why Is Social Data Analysis Important?

The interconnectivity of the modern age means there are huge amounts of data created every day, most of it right at your fingertips. But it’s not always easy to analyze data, especially when it’s unstructured.

Imagine your company releases a new product and you see a sudden uptick in tweets about your company.

Good, right? 

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While there are some great social media analytics tools on the market, which dig into your metrics and help you identify patterns, trends, and opportunities, you also need to know which specific tweets are positive or negative, which topics customers mention and why.

Feedback from social media platforms sometimes provides the truest opinions. 

Unsolicited opinions on social media are often where pure admiration or distaste for a product or company is expressed, when customers feel compelled to voice their opinion for others to see, making it a valuable source of feedback.

Text analysis is an advanced machine learning technique that helps you understand, process, and sort social media conversations for polarity (negative to positive sentiment), topic, urgency, intent, and beyond. It allows companies to:

  • Automate customer care tasks, like manually sorting social media conversations and routing them to the correct team to respond to them.
  • Quickly identify negative comments related to topics
  • Monitor social mentions in real time
  • Understand brand sentiment ie., what customers like or dislike about your brand

There could be dozens of conversations happening about your brand on social media right now. Keeping an eye on Twitter using sentiment analysis tools, for example, will help you understand your customers, your competitors, and industry trends.

Top Social Data Analysis Tools

For social media monitoring, machine learning techniques like sentiment classification can be particularly useful. There are approximately 6,000 tweets sent every second. Imagine having access to all that data and the ability to analyze it in real time. 

AI and machine learning programs allow you to scour Twitter for mentions specific to your company and find out exactly how users feel about your brand in minutes. Something that would otherwise take dozens of employees and countless hours to achieve.

Data analysis tools, like MonkeyLearn, can help you get the most out of your social data. By sorting your data by topic, sentiment, and more, you can truly understand what your customers need and start making data-driven decisions.

Social data reveals so much more than just quantitative data. By analyzing the text within this data, you can drive customer segmentation, carry out competitor research, discover marketing opportunities, quickly identify issues related to specific products or services, or unhappy customers that require urgent attention, and so much more.

All with the aim of improving business processes and customer experience. 

Other data analysis tools include: 

  • Brandwatch, which focuses on social influencer and customer listening strategies to provide companies with a full picture of brand sentiment and brand strategy. 
  • Hootsuite, which offers easy integration across all major social networks and provides a holistic view of your data to provide the findings you need to make informed decisions.
  • Social Searcher, which provides a bunch of social analytics but also has a sentiment analysis tool that shows the overall sentiment of social media data and a breakdown of popular posts that have been categorized as negative and positive.

Wrap-Up

Social data analysis can be an extremely helpful tool to evaluate how the public feels about any business. Automated analysis of social media can save hundreds of employee hours and provide more consistent and accurate results. 

There are a number of avenues you can take to gather your data. A proactive approach can keep your company ahead of the trends. Constant customer opinion monitoring will help you stay in direct contact. 

Visit MonkeyLearn to discover advanced social data analysis software.

Rachel Wolff

April 6th, 2020