The use of Natural language processing (NLP), another Artificial Intelligence technique, can turn an unstructured text into a set of features for machine learning to use.
Data-driven, rule-based NLP techniques can extract information from text using linguistic patterns and terminologies with high precision and recall — avoiding the need to manually annotate training data for the machine learning model.
Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences with labels about positivity or negativity), by making a statical inference.
Our powerful AI system develops automatically agents that can learn, and understand the sentiments which arise from the data.
Data-driven, rule-based NLP techniques can extract information from text using linguistic patterns and terminologies with high precision and recall — avoiding the need to manually annotate training data for the machine learning model.
Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences with labels about positivity or negativity), by making a statical inference.
Our powerful AI system develops automatically agents that can learn, and understand the sentiments which arise from the data.