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https://github.com/blacksujit/sentiment-analysis

This project is a sentiment analysis model built to classify IMDB movie reviews as either positive or negative using the **IMDB dataset**. It uses various machine learning models and deep learning techniques to handle the text data.
https://github.com/blacksujit/sentiment-analysis

challenge deep-learning deep-neural-networks lstm-neural-networks model-accuracy nlp nlp-datasets nlp-machine-learning polarity-integration proficient rqs score sentiment-analysis sentiment-classification

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This project is a sentiment analysis model built to classify IMDB movie reviews as either positive or negative using the **IMDB dataset**. It uses various machine learning models and deep learning techniques to handle the text data.

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README 

        
# IMDB Movie Reviews Sentiment Analysis:



This project is a sentiment analysis model built to classify IMDB movie reviews as either positive or negative using the **IMDB dataset**. It uses various machine learning models and deep learning techniques to handle the text data.



## Table of Contents:



- [Overview](#overview)

- [Dataset](#dataset) 

- [Models](#models)

- [Aspect-Based Sentiment Analysis](#aspect-based-sentiment-analysis)

- [Results](#results)

- [Video Explanation](#VideoExplanation)



## Project Video Demo:



[Click To Watch the video demonstration](https://go.screenpal.com/watch/cZQv6qVS7Qa)



## Overview:



This project performs binary classification on the IMDB dataset, where movie reviews are classified into positive (1) and negative (0) sentiments. It uses various machine learning algorithms and an LSTM-based deep learning model for comparison.



The steps involved:

1. Preprocess the data (tokenization, vectorization, and padding).

2. Build models using different algorithms.

3. Train the models.

4. Evaluate their performance.

5. Save the best model for future predictions.



## Dataset:



The dataset used is the **[IMDB movie reviews dataset]()** consisting of 50,000 reviews split equally between training and testing sets.



- **Positive reviews**: 25,000

- **Negative reviews**: 25,000



The dataset is split into training and validation sets for model training and evaluation.



## Models:



We tested the following machine learning models on the dataset to determine their performance:



### 1. **Naive Bayes**

   - Model: Multinomial Naive Bayes with TF-IDF vectorization.

   - **Accuracy**: 85.55%



### 2. **Logistic Regression**

   - Model: Logistic Regression with TF-IDF vectorization.

   - **Accuracy**: 89.29%



### 3. **Naive Bayes (Small Dataset)**

   - Model: Multinomial Naive Bayes on a subset of the dataset.

   - **Accuracy**: 84.60%



### 4. **Gradient Boosting**

   - Model: Gradient Boosting Classifier with TF-IDF vectorization.

   - **Accuracy**: 80.35%



### 5. **LSTM (Deep Learning Model)**

   - Model: An LSTM (Long Short-Term Memory) neural network to capture sequential dependencies in text data.

   - **Accuracy**: 64.27%



### Model Architecture (for LSTM):

1. **Embedding Layer**: Converts integer-encoded words into dense vectors.

2. **LSTM Layer**: Captures long-term dependencies in review sequences.

3. **Dropout Layer**: Helps prevent overfitting.

4. **Dense Layer**: Sigmoid-activated output layer for binary classification.



## Aspect-Based Sentiment Analysis



In addition to traditional sentiment analysis, this project explores **Aspect-Based Sentiment Analysis (ABSA)**, which focuses on identifying sentiments related to specific aspects of the movies. For example, a review might express a positive sentiment towards the acting but a negative sentiment towards the plot. This allows for more granular insights, such as:



- **Improving Movie Attributes**: By understanding which aspects of a movie are well-received and which are not, filmmakers and marketers can make informed decisions on what to emphasize in future projects.

- **Targeted Recommendations**: Users can receive recommendations based on specific attributes they care about (e.g., great cinematography or compelling storylines).

- **Enhanced Customer Feedback**: Businesses can better understand customer feedback and improve their products based on specific strengths and weaknesses highlighted in reviews.



## Results



The LSTM model is a simple neural network architecture, achieving a lower accuracy compared to traditional machine learning models like Naive Bayes and Logistic Regression. Below is a summary of the model performances:



- **Naive Bayes**: 85.55%

- **Logistic Regression**: 89.29%

- **Naive Bayes (Small Dataset)**: 84.60%

- **Gradient Boosting**: 80.35%

- **LSTM**: 64.27%



The best performing model in this case was **Logistic Regression** with an accuracy of **89.29%**.



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### Review Quality Score (RQS)



In this analysis, we also incorporate a **Review Quality Score (RQS)**, which measures the quality of reviews based on various factors such as length, sentiment strength, and engagement metrics (like the number of likes). The benefits of using RQS include:



- **Quality Over Quantity**: By focusing on reviews with higher RQS, the model can leverage more insightful data, leading to better predictions.

- **Filtering Noise**: Lower quality reviews can skew sentiment analysis, but using RQS helps filter out less informative reviews.

- **Informed Model Training**: Higher quality reviews contribute to more robust training data, potentially improving model accuracy.



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