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Sentimental Analysis in Machine Learning

Sentimental Analysis helps in quickly analyzing the numerous amount of data.

Since Artificial Intelligence and its advanced technologies have started influencing different sectors. A lot of research work is taking place for developing different tools that can evolve Artificial Intelligence and Machine Learning more stronger. And Sentiment Analysis is one such topic that has created a buzz in the field of scientific and market research in the field of Natural Language Processing and Machine Learning with the help of its amazing applications. Basically, Sentiment Analysis is a Machine Learning tool. Here different models are trained with the help of different examples like depicting the emotion from the text so that the machine can automatically learn to detect and differentiate emotions without any human interference. Hence many AI Startups from different Sectors have leveraged themselves or are looking forward to leveraging themselves with it.

Working of Sentiment Analysis

In this tool of Machine Learning different techniques are taken into consideration depending upon the output. Also to train the system complex algorithms are used so that the tool of Machine Learning gives us the exact result in Sentimental Analysis techniques. As we have no that every algorithm has its own specifications but when we use them with each other we are able to get the expected results. So let us look into few algorithms that are commonly used,

  • Navis Bayes

One of the most important and popular algorithms from probabilities is extremely useful in getting the outputs in a yes or no format.

P(A/B)= P(B/A) x P(A) / P(B)

  • Support Vector Machines (SVM)

This algorithm is extremely useful in training and classifying the text as it is a supervised machine learning model. It's quite well known and extremely useful here.

  • Linear Regression

Again a simple but extremely helpful machine learning statistical algorithm which is completely suitable for sentimental analysis. As it predicts the output Y depending upon the given X features as inputs.

  • Deep Learning

One more subfield of artificial intelligence and Machine Learning performs amazingly well just like the human brain with the help of artificial neural networks.

Hence we can conclude that the application of Sentimental Analysis can give businesses insight into understanding customer behavior and their moods. As it can be broken into a number of segments it can give us a better understanding of market sentiments. Hence helping businesses in proper planning and execution and also reducing the risk factor.



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