“The shift from scoring overall posts to scoring individual entities allows users to analyze the sentiment of multiple terms and phrases more reliably and accurately.”
The desire to measure social sentiment at a more granular level has risen rapidly within the social media industry. How do you gauge the sentiment of a long-form post, or even a Tweet conveying mixed signals? How can you group terms and phrases together to gain a greater understanding of sentiment within unique and flexibly defined categories relevant to your industry and company?
A better, more accurate scoring mechanism is needed. That mechanism is Sentity.
The Introduction of Sentity
The process of scoring, recording, and reporting sentiment on social media has been changed dramatically with the introduction of Tracx’s Sentity engine. Sentiment classification is no longer based on language cues across the entire post, as users can now define specific keywords and phrases for sentiment tracking called “entities.” With Sentity, greater accuracy and deeper analysis are gained by looking at smaller pieces of language surrounding each mention of these user-defined entities. In addition, entities can be grouped into larger categories for greater insights into key focus areas for brands.
Entities are terms representing a single person, place, or thing, about which data can be stored. Entities maintain distinct and separate existence and objective, can be classified, and have stated relationships to other entities. This shift in focus from entire pieces of content to individual entities within each post allows users to analyze the sentiment of multiple terms and phrases within a single piece of content to uncover truly actionable, focused insights.
Sentiment classification is no longer limited to just looking for cues across entire social posts, but instead efficient algorithms start the process by looking at smaller pieces of language surrounding the content, called “entities.”
For instance, in the example above, a traditional sentiment engine would see the words “wonderful” and “thanks,” and score the post as positive. In reality, the Tweet is expressing sarcasm, and actually should be scored as negative sentiment.
But More Importantly…
More importantly, by layering in the use of definable entities, and by tagging the “delayed flight” portion of the post as having a negative sentiment within a “flight status” entity, an organization can then start to gain deeper insight into more granular categories. The Sentity engine provides easy measurement into key areas of a brand, not just an overall score. The above-cited example could have contained more complex messaging, and actually could have been tagged across multiple entities based on user-defined criteria, yielding highly accurate sentiment scores directly aligned with an organization’s KPIs.
By compiling a complex sentiment score through identifying specific entities within the entire post, generalized and inaccurate classifications of expressive tone across a whole post become a thing of the past… Good riddance to vague and inaccurate methods!
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