What are hidden meanings? How does MotivBase decode these meanings?

Hidden meanings are the unspoken, interrelated inferences that consumers make without even knowing it. That is, when people talk about a topic with others, without even knowing it, they might be communicating things about their unspoken motivations, values, fears etc. to one another that aren't immediately obvious, especially to most technologies that scrape and study online conversations. 

 

Why? 

 

When you search a topic in most analytics/big data tools, you actually instruct the machine to only study those data points/posts where the consumer specifically mentions your topic. For example, if you search for non-GMO most technologies will specifically analyze those conversations or data points where non-GMO was mentioned. It's a literal translation of your request. This is fine if the purpose of the technology is to identify and recognize patterns on what people say. Not, why they say it. 

This is where MotivBase differs significantly. In order to uncover the hidden meanings behind topics and truly conduct an ethnographic analysis, MotivBase studies the full context around a topic. Which means, it examines all those other conversations where non-GMO wasn't specifically mentioned, but the conversation occurred in the same context as the post on non-GMOs (e.g. someone responded to the post on non-GMOs and talked about organic food, but didn't specifically mention non-GMOs). ​​

By conducting a contextual analysis of a topic, MotivBase not only understands new hidden meanings developing around the topic but also identifies when certain meanings are becoming more prominent and as a result, pushing the underlying topic to maturity. 

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