With an unimaginable number of comments found online, identifying and sorting the relevant ones is no easy task. Our web crawlers are able to identify and source the comments that are relevant to a given product category, ensuring the data set is complete and accurate.
After the data has been collected and sorted during the crawling phase, AI analysis is conducted to derive themes, topics, and sentiment for every brand. Various Natural Language Processing (NLP) algorithms allow for all of the text that's been written about brands and products to be simmered down into bite-sized peices.
During the curation phase, the data is cleaned and verified to ensure all relevant brands and products are represented in the product category's data set. The crawlers aren't perfect (yet), so when a new category is added, we make sure anything that was missed or incorrectly included is all sorted out.
This is the final phase and the part that turns all of this fancy AI powered analysis into something that a typical brand manager, product developer, or marketer can understand and work with. You don't need to be a data scientist or a research wizard to start identifying important trends and topics within your product categories.
After each of these steps have been completed, the process begins again to accommodate any new consumer comments. There is a continuous flow of new comments that affect the overall sentiment and topic scores over time. When new comments are made, they're crawled, analyzed, and the data is added to the visualizations.
Without the help of automated processes and artificial intelligence, gaining insights from consumer-generated content would simply be unrealistic, due to the incredible rate of creation and quantity of data consumers add to the internet every day.
Learn more about how Predicta applies AI to consumer-generated content to deliver valuable market insights to brands.