Write any four applications of Natural Language Processing?
Answers
Answer:
☆ 1. Machine Translation
Machine Translation is the subfield of computer linguistics that involves the use of software applications to translate text or speech from one language to another. One of the best examples of machine translation is Gôogle Translate which is known to translate over 100 billion words every day.
☆ 2. Conversational User Interface
A conversational user interface is an interface for computers that emulates a conversation with a real human. For example, a chatbot.
☆ 3. Text Prediction
Text prediction refers to the process of estimating the next word in a phrase or sentence. One of the popular and common examples of text prediction is Gôogle Search.
☆ 4. Sentiment Analysis
Sentiment analysis is the process of interpreting and classifying emotions within text data. Usually having business-specific applications, sentiment analysis allows businesses to identify the sentiment of a customer towards services, brands, or products in online feedback (positive, negative, neutral). For its exceptional abilities, sentiment analysis is used in product analytics, market research, reputation management, precision targeting, marketing analysis, public relations, net promoter scoring, etc.
☆ 1. Machine Translation
⟹ Machine Translation is the subfield of computer linguistics that involves the use of software applications to translate text or speech from one language to another. One of the best examples of machine translation is Gôogle Translate which is known to translate over 100 billion words every day.
☆ 2. Conversational User Interface
⟹ A conversational user interface is an interface for computers that emulates a conversation with a real human. For example, a chatbot.
☆ 3. Text Prediction
⟹ Text prediction refers to the process of estimating the next word in a phrase or sentence. One of the popular and common examples of text prediction is Gôogle Search.
☆ 4. Sentiment Analysis
⟹ Sentiment analysis is the process of interpreting and classifying emotions within text data. Usually having business-specific applications, sentiment analysis allows businesses to identify the sentiment of a customer towards services, brands, or products in online feedback (positive, negative, neutral). For its exceptional abilities, sentiment analysis is used in product analytics, market research, reputation management, precision targeting, marketing analysis, public relations, net promoter scoring, etc.