Utilizing social listening tools for comprehensive research significantly enhances the ability to gather and analyze feedback from users or consumers on particular topics, brands, or public figures. When faced with extensive datasets, identifying specific themes or subtopics within the conversations presents a complex challenge. This is precisely the context in which "Tags" prove to be exceedingly useful.

The primary function of "Tags" is to systematically categorize conversations into distinct subtopics, thereby facilitating a deeper understanding of user or consumer perceptions and sentiments. They allow for the performance of targeted sub-queries on the primary query outcomes, thus illuminating the various subjects being discussed, particularly when there is prior knowledge of the potential topics of interest.

The tagging system is different from AI Topic Analysis. The tagging system is a more manual, user-defined method of categorizing social media content. It involves assigning specific labels or "tags" to individual pieces of content based on predefined criteria, while the AI topic analysis employs sophisticated algorithms, including natural language processing (NLP) and machine learning (ML), to automatically identify and categorize topics discussed in social media content. Usually, the AI modules aim to define new topics while tags run on predefined topics. 

To effectively deploy "Tags," one must apply specific filters or rules to the gathered data. For example, the digital content matches certain keywords to have a specific tag. For brand X, we might want to know and filter out the content about prices that match the following keywords of the total content:

(السعر OR سعر OR بكام OR غالي OR Prices OR Cost OR Costs OR "How much" OR pricing OR overpriced OR Payment) 

Crowd Analyzer enables creating "Tags" in efficient and user-friendly ways. By adhering to a sequence of straightforward steps. Crowd Analyzer offers two types of “Tags”, the first one is “Smart Tagging” which applies tags on the futuristic mentions, and the second is “Manual Tagging”, which can be used to tag historical data. 

For example, if we applied “Smart Tagging” on the keywords above, the feature will tag the mentions that will be captured after the tag creation date. Any brand content that has one of the keywords above will automatically be tagged “Price”. But if we need to use the same tags on historical data, here comes the “Manual Tagging” which can be done in bulk using CA dashboard features.

Tags are very helpful when it comes to categorizing conversations into topics to be easily monitored, tracked, and analyzed which allows users to see the differences between topics, and which of them is driving the conversations. For example, Price,  Quality, Availability, Customer Service, Branches. 

In the rapidly evolving social media listening world, effective social media intelligence is crucial for businesses and individuals alike to move data into insights quickly. "Tags" in social listening emerge as a powerful tool in this domain, offering precision and clarity. By enabling users to categorize and analyze conversations with pinpoint accuracy, Tags empower decision-makers with actionable insights, leading to more informed strategies and decisions.