AI social listening tools need to gather and analyze millions of social conversations scattered across social networks. Refining this immense data can get challenging unless the tool has robust machine learning algorithms.
Inability to identify jordan business email list sentiment contextually
Social listening may not always correctly understand the sentiment of customer feedback. Unless the tool has been trained on phrases and idioms that mean sarcasm or irony, it can misinterpret messages.
Inaccurate insights
The complexity of human language and the way social messages are written often prove problematic for social listening tools. They’re often unable to understand double negatives or emojis. Plus, the tool may be limited due to data size and social network restrictions, resulting in the inability to give actionable results.
Overcoming AI social listening challenges
To overcome these problems, AI tools need strong NLP and ML algorithms, extensive data integration and powerful aggregation capabilities. What does this mean? Let’s dig in to understand.
Natural language processing
NLP algorithms combine several technologies such as sentiment analysis, named entity recognition (NER) and semantics to understand the context and nuances of social conversations, including slang and cultural references. This powers effective filtering, which enables a tool like Sprout to give accurate results through capabilities like our Query Builder.