movie review sentiment analysis project

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This project demonstrates a complete pipeline for sentiment analysis on a dataset of 50,000 IMDB movie reviews. Using a Long Short-Term Memory (LSTM) neural network, this code classifies movie reviews as either positive or negative. The project includes data preprocessing, model training, evaluation, and a function for sentiment prediction.

Harsh-C7/IMDB-Reviews-Sentiment-Analysis

Folders and files, repository files navigation, imdb movie reviews sentiment analysis, project overview, 1. dataset access and preparation.

• Kaggle Integration : The code starts by loading the kaggle.json file to set up Kaggle API credentials and download the dataset ( IMDB Dataset of 50K Movie Reviews ).
• Data Extraction : The dataset is extracted from a ZIP file, and a pandas DataFrame is created from the CSV file.
• Label Encoding : The 'sentiment' column is transformed into numerical labels using LabelEncoder , where 'positive' is encoded as 1 and 'negative as 0`.
• Data Splitting : The dataset is split into training (80%) and testing (20%) sets for model evaluation.

2. Text Preprocessing

• Tokenization : The Tokenizer from Keras is used to tokenize the text reviews, restricting to the top 5,000 most common words.
• Padding : Reviews are converted to sequences of integers and padded to a uniform length of 200 words to ensure consistent input size.

3. Model Architecture

• Embedding Layer : Converts integer-encoded words into dense vectors of fixed size (128).
• LSTM Layer : A recurrent layer with 128 units that helps in capturing long-term dependencies in the text. Dropout and recurrent dropout are set at 0.2 to prevent overfitting.
• Dense Layer : A single unit with a sigmoid activation function outputs a probability score for binary classification.

4. Model Compilation and Training

• Compilation : The model uses binary_crossentropy as the loss function, adam optimizer, and tracks accuracy as the metric.
• Training : The model is trained with a batch size of 128 over 5 epochs and validated using 20% of the training data.

5. Model Evaluation

• Performance Metrics : The model is evaluated on the test set, reporting a loss of approximately 0.334 and an accuracy of around 86.57%.

6. Sentiment Prediction Function

• Functionality : The predict_sentiment function takes a raw text review, processes it through the trained tokenizer, and returns a prediction of either "Positive" or "Negative" based on the output probability.

Results and Conclusion

The LSTM model achieved a test accuracy of approximately 86.57% , demonstrating its effectiveness for text classification tasks.

• Jupyter Notebook 100.0%

Sentiment Analysis of IMDB Movie Reviews using Convolutional Neural Network (CNN) with Hyperparameters Tuning

Alireza bagheri, table of contents.

• Load IMDB movie reviews
• Decode reviews from index
• Truncate and pad the review sequences
• Build the model
• Create the model
• Tune hyperparameters
• Train the model
• Evaluate the model

Data ¶

In this project, I will use IMDB movie reviews. This dataset contains 50,000 movie's reviews from IMDB, labeled by sentiment (positive/negative). The dataset can be loaded and splitted into training and test sets as the following.

Load IMDB movie reviews ¶

Let us have a look at the first sample of training set.

As it clear, the text of reviews is integer-encoded, where each integer represents a specific word in the dictionary.

Decode reviews from index ¶

We can convert the integers back to words as the following.

In continue, I will only consider the top 5,000 most common words. I will also consider 20% of the training set for validation purpose.

Let us inspect how the first review looks like when we only consider the top 5,000 frequent words.

Truncate and pad the review sequences ¶

Movie reviews can be different lengths. We will use the pad_sequences function to standardize the lengths of the reviews.

Let us check the first padded review.

Build the model ¶

Create the model ¶.

In this project, I will consider a Convolutional Neural Network (CNN) for the text classification.

Tune hyperparameters ¶

Now, it is time to tweak hyperparameters to imporve accuracy over validation set.

Train the model ¶

Here, I train the model with the best obtained hyperparameters over train + validation sets.

Evaluate the model ¶

Finally, I evaluate performance of the trained model over unsean test set.

Reference ¶

https://keras.io/examples/imdb_cnn/

Movie Reviews Using Sentiment Analysis

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