6 Real-World Examples of Natural Language Processing

These approaches are also commonly used in data mining to understand consumer attitudes. In particular, sentiment analysis enables brands to monitor their customer feedback more closely, allowing them to cluster positive and negative social media comments and track net promoter scores. By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly. Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language.

​Government agencies are awash in unstructured and difficult to interpret data. To gain meaningful insights from data for policy analysis and decision-making, they can use natural language processing, a form of artificial intelligence. NLP is used in consumer sentiment research to help companies improve their products and services or create new ones so that their customers are as happy as possible.

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Getting a language learning partner is one method this and was already pointed out earlier. Negative emotions can put a noticeable hamper on language acquisition. When a learner is feeling anxious, embarrassed or upset, his or her receptivity towards language input can be decreased. This makes it harder to learn or process language features that would otherwise be readily processed.

The global natural language processing market has been segmented into component, deployment, application, vertical, and region. The healthcare segment is expected to expand at a substantial CAGR during the forecast period. The growing use of automation tools in this industry is a major factor leading to segment growth.

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NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own. A natural language processing expert is able to identify patterns in unstructured data. For example, topic modelling (clustering) can be used to find key themes in a document set, and named entity recognition could identify product names, personal names, or key places. Document classification can be used to automatically triage documents into categories.

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