Machine Learning Models for Sentiment Analysis in Python: Naive Bayes
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Sentiment analysis, also known as opinion mining, is an essential application of Natural Language Processing (NLP) that involves identifying and extracting subjective information from text data. This technique is widely used to determine the sentiment or emotion of a piece of text towards a particular topic or product. In python, we can implement sentiment analysis using various machine learning models. The Naive Bayes classifier is an attractive choice for this task due to its simplicity and effectiveness.
First, we will prepare the data. It is essential to clean the text data and perform Data Preprocessing, which includes removal of stopwords, stemming, and lemmatization. We then convert our text data into a bag of words or term frequency-inverse document frequency (TF-IDF) matrix. This matrix will be used as our feature set to train and test the model.
After preparing the data, we will implement the Naive Bayes classifier. We will start by importing required libraries, Naive Bayes classifier and necessary data pre-processing tools. Once the data is prepared, we will fit and train the model on our dataset. We will also discuss the evaluation metrics like accuracy, precision, recall and F1-score to determine the performance of our model.
As a continuation of this series, we will explore other machine learning models such as Support Vector Machines (SVM) and Logistic Regression for sentiment analysis, offering a comparative analysis of their strengths and weaknesses.
Additional Resources:
– Scikit-learn Naive Bayes documentation: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
– TextBlob library: https://textblob.readthedocs.io/en/dev/
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