One of the most talked about capabilities of social media monitoring platforms is sentiment analysis, more specifically the automation of same. It’s a technology that’s important and can be valuable to companies’ social media analysis, but it’s critical to understand how it works, when it’s useful, and what its limitations are.
At Radian6, we’ll be publicly releasing our automated sentiment capabilities inside the platform early next week, and they’ll be immediately available to all current and new customers. As a bit of background, however, we thought we’d talk a bit about what we see as the role of automated sentiment in social media monitoring and engagement, and how Radian6 is approaching it.
What is Automated Sentiment technology, and How Does Radian6 Use It?
Automated sentiment analysis is a system for automatically determining the sentiment of a sentence or phrase. Sentiment refers to the thought or mood of a post and can be either positive, neutral or negative.
Radian6 automated sentiment reviews on-topic posts as they come in, determines the sentiment of the post at the sentence level, and aggregates a positive, negative, or neutral designation at the post level based on specified sentiment keywords and phrases. If a particular document or post touches multiple topics, sentiment can be determined for each separate topic.
Stay tuned on the Radian6 blog next week for more detail on Radian6’s automated sentiment capabilities.
What Automated Sentiment Can Help With
As a first pass, automated sentiment analysis can help streamline the workflow of processing a high volume of posts by providing preliminary determinations of sentiment for each post. Users can then follow up with review and manual adjustment as necessary. Automated sentiment also provides an initial snapshot of postive-negative-neutral ratios, and can help identify trends at a macro level such as sparklines or aggregate changes in sentiment over time.
Looking at ratios of positive to negative sentiment over time can sometimes indicate collective brand preferences as expressed online, or the overall mindset or mood of audiences. The unfiltered and unedited nature of the opinions expressed on the social web and tracked through sentiment analysis can sometimes offer a more realistic, less clinical view of how customers and communities are responding to companies and brands.
Armed with this high level analysis and trend information, Radian6 users can better craft engagement strategy, understand hot button issues and topics around their brand, and reach out to their customers informed about the pulse of opinion about the company and it’s work.
Automated Sentiment and The Human Factor
Sentiment analysis, whether automated or manual, is a subjective process and always needs to be considered in the context of business goals.
What’s read as positive for one person or in one context might be considered neutral for another, so businesses need to consider and outline criteria for positive, negative, and neutral definitions based on their goals for online presence and engagement.
In addition, the complexity and nuance of the English language combined with available technologies for text analytics means that sentiment analysis cannot currently achieve 100% accuracy. In fact, accuracy rates across sentiment analysis engines can be highly variable, as the criteria to define an “accurate” sentiment determination is also somewhat dependent upon human interpretation and context.
There will always be a need for human review and involvement to verify automated results, and ensure that sentiment levels are tagged within the context of individual and unique business goals and agreed upon criteria.
For More on Radian6 Sentiment Analysis…
Stay tuned for a post early next week with some additional detail about the technical features of Radian6’s automated sentiment, and information on how to set it up inside your Radian6 dashboard. And as always, if you have questions or feedback for our team regarding this or any other feature, we’d welcome your input and conversation.
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On Automated Sentiment Analysis