We have seen a huge growth in technology in recent years. The use of smartphones is increased rap... more We have seen a huge growth in technology in recent years. The use of smartphones is increased rapidly no matter where you go you find at least one smartphone in every family; social networking sites have seen incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of data is generated day by day around 70% of world data are generated by social media sites. Unlike any other social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a machine learning method in which the model of the training data is present only in theory until you made any prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will be selected for further work.
International Research Journal of Modernization in Engineering Technology and Science , 2021
We have seen a huge growth in technology in recent years. The use of smartphones is increased rap... more We have seen a huge growth in technology in recent years. The use of smartphones is increased rapidly no matter where you go you find at least one smartphone in every family; social networking sites have seen incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of data is generated day by day around 70% of world data are generated by social media sites. Unlike any other social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a machine learning method in which the model of the training data is present only in theory until you made any prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will be selected for further work.
We have seen a huge growth in technology in recent years. The use of smartphones is increased rap... more We have seen a huge growth in technology in recent years. The use of smartphones is increased rapidly no matter where you go you find at least one smartphone in every family; social networking sites have seen incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of data is generated day by day around 70% of world data are generated by social media sites. Unlike any other social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a machine learning method in which the model of the training data is present only in theory until you made any prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will be selected for further work.
International Research Journal of Modernization in Engineering Technology and Science , 2021
We have seen a huge growth in technology in recent years. The use of smartphones is increased rap... more We have seen a huge growth in technology in recent years. The use of smartphones is increased rapidly no matter where you go you find at least one smartphone in every family; social networking sites have seen incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of data is generated day by day around 70% of world data are generated by social media sites. Unlike any other social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a machine learning method in which the model of the training data is present only in theory until you made any prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will be selected for further work.
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Papers by Nawraj bhatt
matter where you go you find at least one smartphone in every family; social networking sites have seen
incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of
data is generated day by day around 70% of world data are generated by social media sites. Unlike any other
social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment
analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this
paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But
first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a
machine learning method in which the model of the training data is present only in theory until you made any
prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local
regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In
Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes
Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will
be selected for further work.
matter where you go you find at least one smartphone in every family; social networking sites have seen
incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of
data is generated day by day around 70% of world data are generated by social media sites. Unlike any other
social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment
analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this
paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But
first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a
machine learning method in which the model of the training data is present only in theory until you made any
prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local
regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In
Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes
Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will
be selected for further work.