Advanced NLP techniques that guide modern data mining
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Table of Content ● What Is Natural Language Processing (NLP)? ● What Are Top Advanced NLP Techniques? ● Leverage NLP Technology With Repustate
Overview Advanced NLP techniques like named entity recognition (NER), semantic search, text summarizations, sentiment analysis, are top NLP technology tasks that guide the modern data mining industry. In this article we will take a look at these and other important NLP techniques including aspect-based granularity, machine translations and more.
What Is Natural Language Processing (NLP)? Natural language processing (NLP) is a sub-task of artificial intelligence that analyzes human language comprising text and speech through computational linguistics. It uses machine learning and deep learning models to understand the intent behind words in order to know the sentiment of the text. NLP is used in speech recognition, voice operated GPS phone and automotive systems, smart home digital assistants, video subtitles, sentiment analysis, image recognition, and more.
What Are Top Advanced NLP Techniques?
Leverage NLP Technology With Repustate
A sentiment analysis API uses almost all of the advanced NLP techniques mentioned above and more, to help brands find the information they need to increase their productivity and return on investment. Repustate’s has all-in-one sentiment analysis dashboard, Repustate IQ, uses natural language processing and machine learning to process data in all formats - video, audio, text, and images - to give the most accurate results. Its multilingual capability stems from part-of-speech taggers that are uniquely built for each of the 23 languages it supports. Repustate IQ does not use translations to convert one language into another for the purpose of analysis, but rather reads the data natively. Furthermore, its NER capability is highly advanced and extremely rich, supporting all major industries including retail, government, banking, automotive, hospitality, finance, and others. The platform reads social media listening data seamlessly and as easily as it does video or document thanks to advanced NLP techniques. The solution understands all the complexities of social media such as abbreviations, special characters, hashtags, code switches, emojis, and colloquialisms.
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