Dataset: https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews/data

This Python Code using a Naive Bayes classification on a dataset of movie reviews to perform sentiment analysis.

• pandas
• sklearn
• string

Reads a CSV file containing movie reviews and their sentiments (positive or negative) into a Pandas DataFrame

• defines a function to clearn and preprocess the text data
• remove punctuations, converts text to lowercase, tokenizes the text, and remove English stopwords

• use the CountVectorizer from sklearn.feature_extracttion.text to convert the text data into a bag-of-words representation
• create a pipelines that includes CountVectorizer and a Multinomial Naive Bayes classifier
• trains the model on the training set ('X_train' and 'y_train')

• use the trained model to make predictions on the test set ('X_test')
• calculate and print the accuracy and classification report of the model

• loads the movie reviews dataset
• preprocesses the text data in the 'review' column of the DataFrame
• splits the dataset into the training and testing sets
• trains the model and evaluates its performance
• asks the user to input a new movie review and predicts its sentiment using the trained model

This Python code implements a simple Naive Bayes classifications for sentiment analysis.

• pandas
• string
• Counter from collections

Reads a CSV file containing movie reviews and their sentiments (positive or negative) into a Pandas DataDrame

• defines a function to remove punctuation and tokenize the text
• applies this function to the 'review' column of the DataFrame, storing the result as a list of words

Splits the dataset into training and testing sets, using 80% for training and 20% for testing

• defines a simple Naive Bayes classifier class
• the 'train' method calculates class probabilities and word probabilites based on the training data
• the 'predict' method predicts the sentiment of a given review based on the trained probabilities

• creates an instance of the Naive Bayes classifier class
• trains the classifier using the training data

uses the trained classifier to make predictions on the test data

• compares the predicted sentiments with the actual sentiments in the test data to calculate accuracy
• prints the accuracy of the model on the test data

• take a new movie review as input from the user
• preprocesses the input review
• uses the trained classifier to predict the sentiment of the new review
• prints the predicted sentiment

This Python code performs sentiment analysis on movie reviews using a deep learning model

• pandas
• sklearn
• tensorflow
• string

Reads a CSV file containing movie reviews and their sentiments (positive or negative) into a Pandas DataFrame

• defines a function to remove punctuation and convert text to lowercase
• applies this function to the 'review' column of the DataFrame, preprocessing the text data

splits the dataset into training and testing sets, using 80% for training and 20% for testing

• initializes a tokenizer to convert text data to sequences of integers
• initializes a label encoder to encode sentiment labels into numerical format

• defines a function that takes training data and labels and returns a trained deep learning model, tokenizer, and label encoder
• tokenizes and pads the input reviews
• builds a sequential model with an embedding layer, a flatten layer, and a dense layer
• compiles and trains the model using binary cross-entropy loss and the Adam optimizer

defines a function that takes a trained model, label encoder, test data, and labels, and returns the accuracy of the model on the test set

• loads the data, preprocesses the text, and splits it into training and testing sets
• calls the 'train_model" function to train the deep learning model
• calls the 'evaluate_model' function to evaluate the model on the testing set
• takes a new movie review as input, preprocesses it, tokenizes and pads it, and uses the trained model to predict the sentiment
• prints the accuracy on the test set and the predicted sentiment for the new review