More than tracking how often your brand appears online, social media sentiment data measures the feelings people have for your brand—and how passionate they are about it.
Social media sites and applications like Facebook, Instagram, and Twitter are the main sources of sentiment data. Other sites with this data include blogs, forums, and review pages.
More specifically, sentiment data analyzes the likes, comments, mentions, shares, and impressions from the above sources. It also analyzes images and related keywords and key phrases from these sources.
All sentiment analysis tools measure social media and websites for positive and negative feelings toward or about a brand (or topic) and many now include neutral sentiment as well. Many tools, however, go further by determining the strength of people’s sentiments.
Most data vendors present sentiment information as pie or bar graphs. However, vendors that offer more detailed analyses measuring keywords across an industry or entire market may use cluster graphs. These are graphs made up of nodes, each of which represents a single keyword. The size of the node indicates the keyword use volume. The location of the node, meanwhile, represents how central it is to the topic.
Companies use sentiment data to track their brand’s performance overall and within a target market. This data allows companies to analyze their market and their competitors, plan new products, measure campaign performance, and manage potential PR crises.
Many data providers, such as the ones on our site, provide custom social media sentiment analysis. If you choose to partner with one of them, just make sure they provide constant, ideally real-time, updates and use the latest NLP (natural language processing) AI programs.
If you choose to create your own social media sentiment data, focus on NLP programs, especially Naive-Bayes analysis. (That is not to say that support-vector machines or decision trees are anything but excellent resources.) Ensure that you use a wide range of social media and online sources and that your dataset updates as often as possible.
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VERN features a “revolutionary” new model of communications, an incongruity detector, and a machine learning capability to refine the preliminary scores into a final emotive score. Available through an API, it allows companies that have AI systems to use VERN to analyze the emotional content within their messages.
“Our competitors offer users only positive, negative, neutral, or mixed over an entire message,” Tucker says. “VERN analyzes each sentence for emotion and gives the emotion over the entire message.”
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X-Byte’s dataset – ‘X-Byte Social Media Data | Social Media Analytics | Scrape Data from Social Media Websites’ provides Social Data, Individual Data and Social Media Sentiment Data that can be used in Predictive Maintenance, Behavioral Targeting, Trend Forecasting, and Online and Social Media Performance Tracking
The Data Appeal Company’s dataset – ‘Data Appeal’s Fair Index – CSR audience data Europe’ provides Company Data, and Social Media Sentiment Data that can be used in
SocialSentiment.io’s dataset – ‘SocialSentiment.io – Social media sentiment analysis of posts related to stocks – 30 days of history API’ provides Social Data, Social Media Sentiment Data and Stock & Market Data that can be used in Hedge Fund Management, Portfolio Management and