Sentiment Analysis on Movie Reviews

Purpose

This project aims to perform sentiment analysis on movie reviews using machine learning techniques. Sentiment analysis involves determining the sentiment expressed in a piece of text, which can be positive, negative, or neutral. By analyzing movie reviews, we can gain insights into audience opinions and preferences regarding different films.

Functionality

Data Acquisition:
    The project reads movie review data from a CSV file.

Text Preprocessing:
    Preprocesses the text data by:
        Removing HTML tags
        Removing emoticons
        Removing non-alphanumeric characters
        Removing stopwords
        Stemming using the Porter stemming algorithm

Feature Engineering:
    Uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to convert text data into numerical features.

Model Training:
    Trains a logistic regression classifier on the preprocessed data.

Model Evaluation:
    Evaluates the classifier's performance on a test set.

Model Persistence:
    Saves the trained model using pickle for future use.

Machine Learning Models

The main machine learning model used in this project is logistic regression. Logistic regression is a binary classification algorithm that predicts the probability of a given input belonging to a particular class. In this case, it predicts whether a movie review expresses positive or negative sentiment.

Techniques Used

Text Preprocessing:
    Text data is preprocessed using techniques such as:
        Removing HTML tags
        Removing emoticons
        Removing non-alphanumeric characters
        Removing stopwords
        Stemming using the Porter stemming algorithm

TF-IDF Vectorization:
    TF-IDF is used to convert text data into numerical features while giving importance to terms that are frequent in a document but rare in other documents.

Logistic Regression:
    Logistic regression is employed as the classification algorithm due to its simplicity and effectiveness in binary classification tasks.

Results

After training and evaluating the logistic regression model, the following results were obtained:
    Accuracy on the test set: [Insert accuracy score here]
    Other evaluation metrics (if applicable): [Insert other metrics here]

Conclusion

Sentiment analysis on movie reviews provides valuable insights into audience sentiments towards different films. By leveraging machine learning techniques such as logistic regression and TF-IDF vectorization, we can automate the process of analyzing large volumes of text data, enabling faster decision-making in the entertainment industry.