Changes

Sentiment-Analysis-Of-Restaurant-Reviews/README.md → Machine_Learning/Sentiment-Analysis-Of-Restaurant-Reviews/README.md

@@ -1,91 +1,91 @@
-# Sentiment Analysis of Restaurant Reviews
-
-## Overview
-
-This project focuses on sentiment analysis using machine learning techniques. The goal is to classify the sentiment of textual data into positive, negative, or neutral categories. By leveraging various machine learning algorithms, this project aims to provide insights into customer opinions expressed in restaurant reviews.
-
-## Technologies Used
-
-- **Python**: The primary programming language used for data manipulation, model building, and evaluation.
-- **NLTK (Natural Language Toolkit)**: A powerful library for working with human language data (text). It provides tools for text processing, including tokenization, stemming, and removing stopwords.
-- **Scikit-learn**: A robust library for machine learning in Python, which provides tools for model training, evaluation, and feature extraction.
-- **Jupyter Notebook**: An interactive environment for developing and presenting data science projects. It allows for easy experimentation and visualization of results.
-
-## Installation
-
-1. Clone the repository:
-
-   ```bash
-   git clone https://github.com/Tusharb331/Sentiment-Analysis-Of-Restaurant-Reviews.git
-   cd Sentiment-Analysis-Of-Restaurant-Reviews
-
-2. Install dependencies:
-
-   ```bash
-   pip install -r requirements.txt
-
-3. Run the Jupyter notebook Restaurant_Review_Sentiment_Analysis.ipynb to see the step-by-step analysis, preprocessing of data, model training, and evaluation.
-
-## Description
-
-### Data Cleaning
-
-To ensure the accuracy and effectiveness of sentiment analysis, the following preprocessing steps are applied to the reviews:
-
-1. **Removing Special Characters**: Eliminating punctuation and symbols that do not contribute to the sentiment.
-2. **Converting to Lowercase**: Standardizing the text to lowercase to ensure uniformity during analysis.
-3. **Removing Stopwords**: Filtering out common words (e.g., "and", "the", "is") that do not carry significant meaning in sentiment analysis.
-4. **Stemming**: Reducing words to their base or root form (e.g., "running" to "run") to consolidate similar meanings.
-
-### Feature Extraction
-
-The **Bag of Words** model is utilized for feature extraction, transforming text data into numerical feature vectors using **CountVectorizer**. This technique creates a matrix where each row represents a review, and each column corresponds to a unique word from the corpus. The values in the matrix represent the frequency of each word in the respective review.
-
-### Model Training
-
-Three different machine learning models are trained to classify the sentiment of the reviews:
-
-1. **Multinomial Naive Bayes**: A probabilistic model that assumes the presence of a particular feature (word) in a class (sentiment) is independent of the presence of any other feature.
-2. **Bernoulli Naive Bayes**: Similar to Multinomial Naive Bayes, but it assumes binary features (words present or not).
-3. **Logistic Regression**: A regression model used for binary classification that predicts the probability of a binary outcome based on one or more predictor variables.
-
-### Model Evaluation
-
-Each model is evaluated on various performance metrics to assess its effectiveness:
-
-- **Accuracy**: The ratio of correctly predicted instances to the total instances.
-- **Precision**: The ratio of correctly predicted positive observations to the total predicted positives.
-- **Recall**: The ratio of correctly predicted positive observations to the all actual positives.
-
-### Results
-
-The performance of each model is summarized below:
-
-- **Multinomial Naive Bayes**: 
-  - Accuracy: 76.5%
-  - Precision: 0.78
-  - Recall: 0.78
-- **Bernoulli Naive Bayes**: 
-  - Accuracy: 76.5%
-  - Precision: 0.79
-  - Recall: 0.76
-- **Logistic Regression**: 
-  - Accuracy: 75.0%
-  - Precision: 0.82
-  - Recall: 0.68
-
-### Prediction
-
-You can predict the sentiment (positive or negative) of your review messages using the trained models. 
-
-### Example
-
-Here's a simple example of how to use the sentiment analysis functionality in your code:
-
-```python
-from sentiment_analysis import predict_review
-
-msg = 'The food is really good here.'
-prediction = predict_review(msg)
-print(f"Sentiment: {'Positive' if prediction else 'Negative'} Review")
-
+# Sentiment Analysis of Restaurant Reviews
+
+## Overview
+
+This project focuses on sentiment analysis using machine learning techniques. The goal is to classify the sentiment of textual data into positive, negative, or neutral categories. By leveraging various machine learning algorithms, this project aims to provide insights into customer opinions expressed in restaurant reviews.
+
+## Technologies Used
+
+- **Python**: The primary programming language used for data manipulation, model building, and evaluation.
+- **NLTK (Natural Language Toolkit)**: A powerful library for working with human language data (text). It provides tools for text processing, including tokenization, stemming, and removing stopwords.
+- **Scikit-learn**: A robust library for machine learning in Python, which provides tools for model training, evaluation, and feature extraction.
+- **Jupyter Notebook**: An interactive environment for developing and presenting data science projects. It allows for easy experimentation and visualization of results.
+
+## Installation
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/Tusharb331/Sentiment-Analysis-Of-Restaurant-Reviews.git
+   cd Sentiment-Analysis-Of-Restaurant-Reviews
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+
+3. Run the Jupyter notebook Restaurant_Review_Sentiment_Analysis.ipynb to see the step-by-step analysis, preprocessing of data, model training, and evaluation.
+
+## Description
+
+### Data Cleaning
+
+To ensure the accuracy and effectiveness of sentiment analysis, the following preprocessing steps are applied to the reviews:
+
+1. **Removing Special Characters**: Eliminating punctuation and symbols that do not contribute to the sentiment.
+2. **Converting to Lowercase**: Standardizing the text to lowercase to ensure uniformity during analysis.
+3. **Removing Stopwords**: Filtering out common words (e.g., "and", "the", "is") that do not carry significant meaning in sentiment analysis.
+4. **Stemming**: Reducing words to their base or root form (e.g., "running" to "run") to consolidate similar meanings.
+
+### Feature Extraction
+
+The **Bag of Words** model is utilized for feature extraction, transforming text data into numerical feature vectors using **CountVectorizer**. This technique creates a matrix where each row represents a review, and each column corresponds to a unique word from the corpus. The values in the matrix represent the frequency of each word in the respective review.
+
+### Model Training
+
+Three different machine learning models are trained to classify the sentiment of the reviews:
+
+1. **Multinomial Naive Bayes**: A probabilistic model that assumes the presence of a particular feature (word) in a class (sentiment) is independent of the presence of any other feature.
+2. **Bernoulli Naive Bayes**: Similar to Multinomial Naive Bayes, but it assumes binary features (words present or not).
+3. **Logistic Regression**: A regression model used for binary classification that predicts the probability of a binary outcome based on one or more predictor variables.
+
+### Model Evaluation
+
+Each model is evaluated on various performance metrics to assess its effectiveness:
+
+- **Accuracy**: The ratio of correctly predicted instances to the total instances.
+- **Precision**: The ratio of correctly predicted positive observations to the total predicted positives.
+- **Recall**: The ratio of correctly predicted positive observations to the all actual positives.
+
+### Results
+
+The performance of each model is summarized below:
+
+- **Multinomial Naive Bayes**: 
+  - Accuracy: 76.5%
+  - Precision: 0.78
+  - Recall: 0.78
+- **Bernoulli Naive Bayes**: 
+  - Accuracy: 76.5%
+  - Precision: 0.79
+  - Recall: 0.76
+- **Logistic Regression**: 
+  - Accuracy: 75.0%
+  - Precision: 0.82
+  - Recall: 0.68
+
+### Prediction
+
+You can predict the sentiment (positive or negative) of your review messages using the trained models. 
+
+### Example
+
+Here's a simple example of how to use the sentiment analysis functionality in your code:
+
+```python
+from sentiment_analysis import predict_review
+
+msg = 'The food is really good here.'
+prediction = predict_review(msg)
+print(f"Sentiment: {'Positive' if prediction else 'Negative'} Review")
+