For a long time, there have been predictions happening in the financial market, such as the stock market and bond market. Cryptocurrency market has been on rise since the past few years but predictions about the market trend followed by these currencies is still in its nascent state. This study is based on the relationship between current sentiments of public opinion and price variation in this market, and is concerned with predicting whether the cryptocurrency market will trend upward or downward using sentiment analysis, which is one among the various applications of natural-language processing. Bitcoin is the selected cryptocurrency. A platform to predict the future market trend of cryptocurrency market is built using Naïve Bayes Algorithm, in Python language. The validity of the model is tested by finding correlation between sentiment polarity of tweets and with price values of respective time periods. An accuracy of 89.86% is achieved by this model. This article features how Naive Bayes algorithm forecasts and analyze cryptocurrency trends.
The Naive Bayes Algorithm selected to build the prediction platform and analyze cryptocurrency trends in this work is the Naïve Bayes Classification model. Training the classifier using a dataset is an important aspect of supervised learning techniques. A part of the data collected is set apart as a training dataset and the other part is the testing dataset. This training data is given as input to the classifier to make it easier for the prediction of unknown data by this classifier.
A portion of the dataset kept apart as testing data is given as input to the classifier model. When an unknown dataset is given, the classifier will predict accurately if training and testing of data were done properly. In this study, the classification process is done by building a Naïve Bayes classifier that belongs to the family of "probabilistic classifiers".
The Naive Bayes classification technique is based on Bayesian theorem and it is particularly suitable in the cases where dimensionality of the inputs is high. Even if the technique is simple, Naive Bayes can sometimes outperform more sophisticated classification methods. Naive Bayes algorithm mainly works for binary or two-class and multi-class classification problems.
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