Using of Natural Language Processing (NLP) for making decisions and solving problems

A lot of systems use natural language processing (NLP) to help them to make decisions and solve problems. The word “natural” suggests a kind of magic, but in reality NLP is a very straightforward concept. The word “sentiment” implies a kind of human intelligence but is generally used rather pejoratively because it has such an effect over many people. In this post, you will learn what is going on in a large-scale human sentiment analysis system using machine learning

What makes natural language processing useful for sentiment analysis is that it is very flexible and can easily be combined with other applications such as image classification. This means that the technology could also be helpful for a wide range of applications that are sensitive to the emotional tone of language.

The idea of machine learning is that we can incorporate ideas from various types of scientific disciplines on the basis of their relationships with one another. Machine learning focuses on applying machine learning theories to particular domain and problems. When applied to the social, financial, and political systems, machine learning may be viewed as a tool for improving both the accuracy and the usefulness of systems; or, a tool for improving the efficiency of human decision making; or, a tool for improving the accuracy of the economic system

Machine learning techniques, whether applied to the natural language domain or the social or global context, give us the opportunity to identify patterns that might otherwise be hidden from view. When applied to the political system, machine learning could be viewed and even compared to statistical methods and analysis such as probabilistic modeling. However, machine learning is not simply a collection of tools; instead, it is an integrated system that allows for many different applications, often in very dynamic situations

Babi’s “Speech-Related Neural Network” (STN) is the first example to show how deep learning techniques can provide useful information for predicting public opinion in a complex social environment. The model is based on machine learning and natural language processing combined with other tools such as statistical modeling and probabilistic modeling for prediction of speech and language content. The model is also able to predict that there are different kinds of social context (including political, social networking, and natural communities).

The future of this field is about moving from NLP to natural language understanding (NLU), where a deeper connection between the concepts communicated and facts about the world is established. In artificial intelligence, this is considered an AI-hard problem: to be solved, computers have to be as intelligent as people.

But while the future is still in the works, present NLP technologies  can still provide businesses with useful answers to bounded questions.

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