Text annotation comprises an array of annotations such as query, intent and sentiment.
Sentiment Annotation
This type of text annotation evaluates opinions, emotions, and attitudes to offer useful insights that aid in driving important company decisions. Therefore, the data must be right from the beginning. To obtain such data, text annotators are mostly influenced as they can assess moderate and sentiment content on every web platform. Whether it involves reviewing eCommerce and social media sites or reporting and tagging on keywords that are sensitive or profane, humans can be highly valuable to analyse sentiment data as they have a better knowledge about the latest trends, nuances and slang that could either make or spoil the reputation of the company if the message is perceived or stated poorly.
Intent Annotation
Most people prefer interacting with human-machine interfaces and hence machines must be in a position to comprehend the intent of users and the language naturally. If the intent is not identified by a machine, it may not be able to complete the request and the information may have to be phrased once again. When the rephrasing of the information is left unperceived, the question may be handed over to a human staff by the bot. This takes away the sole objective of using a machine. There is a way to distinguish intent through multi-intent data categorization and collection into categories such as confirmation, recommendation, booking and command. Such categories make it simpler for machines to know the actual intent behind the queries and complete the request by finding a resolution for the same.