sentiment analysis on movie review ppt

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This project demonstrates a comprehensive approach to sentiment analysis using the IMDB movie review dataset. By leveraging deep learning techniques with Keras and GloVe word embeddings, the model classifies reviews into positive and negative sentiments.

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Sentiment analysis on movie review data.

This project aims to perform sentiment analysis on the IMDB movie review dataset. It utilizes deep learning techniques, particularly LSTM and Conv1D layers, to classify movie reviews into positive and negative sentiments. The model is built using Keras and GloVe embeddings for word representations.

Project Structure

• Sentiment Analysis of Movie Review using Keras.ipynb : The main notebook containing code for data preprocessing, model building, training, and evaluation.
• dataSentimental/IMDB Dataset/IMDB Dataset.csv : The dataset containing movie reviews.
• dataSentimental/glove/glove.840B.300d.pkl : Pre-trained GloVe embeddings used for word representation.
• trained_model/Sentiment_Analysis/imdb_model.h5 : The saved model after training.

Libraries Used

• TensorFlow & Keras
• scikit-learn

Key Features

• Data Preprocessing : Handling missing values, encoding labels, and data cleaning to remove unwanted characters and contractions.
• Data Visualization : Visualizing the most frequent words in positive and negative reviews using WordCloud.
• GloVe Embeddings : Using pre-trained GloVe embeddings to enhance the model's understanding of word semantics.
• Model Architecture : A combination of Conv1D, LSTM, and Dense layers with dropout for reducing overfitting.
• Model Evaluation : Evaluating model performance using metrics like accuracy and loss.

Clone the repository:

Navigate to the project directory:

Install the required libraries:

Run the Jupyter notebook Sentiment Analysis of Movie Review using Keras.ipynb to preprocess data, train the model, and evaluate its performance.

The model achieved a training accuracy of X% and a validation accuracy of Y%. The loss and accuracy plots indicate the model's learning curve over the epochs.

• Hashir Khan

This project is licensed under the MIT License - see the LICENSE file for details.

• Python 100.0%

Sentiment Analysis of IMDb Movie Reviews: A Comparative Analysis of Feature Selection and Feature Extraction Techniques

• Conference paper
• First Online: 04 March 2022
• Cite this conference paper

• Gahina Karak 16 ,
• Shubham Mishra 16 ,
• Arkadyuti Bandyopadhyay 16 ,
• Pavirala Ranga Sai Rohith 16 &
• Hemant Rathore 16  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 420))

Included in the following conference series:

• International Conference on Hybrid Intelligent Systems

767 Accesses

1 Citations

Humans are social animals who are dependent on the opinions and experiences of others when it comes to choosing a product for themselves. Most people need to seek the reviews of products like movies, web series, and video games before trying them out themselves. It becomes difficult for an average person to scour the correct information because of the large number of reviews present on the internet. Sentiment analysis is often used to obtain helpful information about a review and classify it into positive or negative sentiment. Our main goal in this paper is to construct sentiment analysis models using different feature extraction (count vectorization, TF-IDF, and Word2Vec) and feature selection (mutual information gain and Chi-square) techniques on textual movie reviews. We also study the performance of various classification algorithms for constructing sentiment analysis models over several metrics. We obtained the highest accuracy of \(90\%\) with TF-IDF Vectorization, Chi2 feature selection, and SVM classification algorithm. We also found that feature selection drastically reduces the train test time for almost all the classification models without severely impacting other performance metrics.

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Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification. In: 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 712–717. IEEE (2013)

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Department of CS & IS, Goa Campus, BITS Pilani, Goa, India

Gahina Karak, Shubham Mishra, Arkadyuti Bandyopadhyay, Pavirala Ranga Sai Rohith & Hemant Rathore

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Correspondence to Gahina Karak .

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Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

Ajith Abraham

Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

Patrick Siarry

Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

Vincenzo Piuri

Niketa Gandhi

University of Bari, Bari, Italy

Gabriella Casalino

Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

Oscar Castillo

Ontario Tech University, Oshawa, ON, Canada

Patrick Hung

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Karak, G., Mishra, S., Bandyopadhyay, A., Rohith, P.R.S., Rathore, H. (2022). Sentiment Analysis of IMDb Movie Reviews: A Comparative Analysis of Feature Selection and Feature Extraction Techniques. In: Abraham, A., et al. Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_27

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DOI : https://doi.org/10.1007/978-3-030-96305-7_27

Published : 04 March 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-96304-0

Online ISBN : 978-3-030-96305-7

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Sentiment Analysis

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Sentiment analysis is the process of detecting positive or negative sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. Automatically analyzing customer feedback, such as opinions in survey responses and social media conversations, allows brands to learn what makes customers happy or frustrated. This helps to tailor products and services to meet the needs of its customers. Sentiment analysis models focus on polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad), urgency (urgent, not urgent) and even intentions (interested v. Not interested). Depending on how you want to interpret customer feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs. There are the following types of analysis – Fine-grained Sentiment Analysis, Emotion detection, Aspect-based Sentiment Analysis, Multilingual sentiment analysis. There are different algorithms you can implement in sentiment analysis models, depending on how much data you need to analyze, and how accurate you need your model to be. Sentiment analysis algorithms fall into one of three buckets – Rule-based, Automatic, Hybrid.

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