Can Naive Bayes be used for text classification?

Can Naive Bayes be used for text classification?

Naive Bayes is a learning algorithm commonly applied to text classification. Some of the applications of the Naive Bayes classifier are: (Automatic) Classification of emails in folders, so incoming email messages go into folders such as: “Family”, “Friends”, “Updates”, “Promotions”, etc.

How do you increase Naive Bayes accuracy?

3. Ways to Improve Naive Bayes Classification Performance

1. 3.1. Remove Correlated Features.
2. 3.2. Use Log Probabilities.
3. 3.3. Eliminate the Zero Observations Problem.
4. 3.4. Handle Continuous Variables.
5. 3.5. Handle Text Data.
6. 3.6. Re-Train the Model.
7. 3.7. Parallelize Probability Calculations.
8. 3.8. Usage with Small Datasets.

How can NLP improve accuracy?

8 Methods to Boost the Accuracy of a Model

1. Add more data. Having more data is always a good idea.
2. Treat missing and Outlier values.
3. Feature Engineering.
4. Feature Selection.
5. Multiple algorithms.
6. Algorithm Tuning.
7. Ensemble methods.

What is accuracy in Naive Bayes?

Naive Bayes classifier is the fast, accurate and reliable algorithm. Naive Bayes classifiers have high accuracy and speed on large datasets. Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features. This assumption is called class conditional independence.

How to improve the accuracy of a naive Bayes classifier?

I have implemented a Naive Bayes Classifier, and with some feature selection (mostly filtering useless words), I’ve gotten about a 30% test accuracy, with 45% training accuracy. This is significantly better than random, but I want it to be better.

Which is the best naive Bayes for sentiment analysis?

NLTK Naive Bayes Classification. NLTK comes with all the pieces you need to get started on sentiment analysis: a movie reviews corpus with reviews categorized into pos and neg categories, and a number of trainable classifiers. We’ll start with a simple NaiveBayesClassifier as a baseline, using boolean word feature extraction.

How to improve the accuracy of text classification?

Improve your model my adding bigrams and tri-grams as features. Try doing some topic modelling like latent Dirichlet allocation or Probabilistic latent Semantic Analysis for the corpus using a specified number of topics – say 20. You would get a vector of 20 probabilities corresponding to the 20 topics for each document.

Why is naive Bayes sensitive to overfitting?

More generally, words which appear rarely are more likely to appear by chance, so using them as features causes overfitting. Naive Bayes is very sensitive to overfitting since it considers all the features independently of each other.