Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings
Sentiment analysis, an important task in Natural Language Processing (NLP), focuses on identifying and extracting sentiments from input. With the exponential expansion of digital information, sentiment analysis has recently gained significant attention across various domains. Traditional sentiment a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925000109 |
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| author | Biplov Paneru Bipul Thapa Bishwash Paneru |
| author_facet | Biplov Paneru Bipul Thapa Bishwash Paneru |
| author_sort | Biplov Paneru |
| collection | DOAJ |
| description | Sentiment analysis, an important task in Natural Language Processing (NLP), focuses on identifying and extracting sentiments from input. With the exponential expansion of digital information, sentiment analysis has recently gained significant attention across various domains. Traditional sentiment analysis methods paired with static embeddings often fall short in capturing the deep contextual relationships within text. In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). We provide a comprehensive overview of the creation and assessment of a convolutional learning model especially suited for sentiment analysis of movie reviews using a dataset of around 50k entries. The proposed approach preprocesses movie reviews, employs RoBERTa to generate rich contextual embeddings, and processes these embeddings through a simple yet effective R-CNN architecture. We perform comprehensive analysis of the R-CNN model, showing a superior test accuracy of 91.5 %, achieving the best results compared to the baseline. Additionally, we develop a Flask-based application, demonstrating the practical applicability of our R-CNN model for real-time sentiment prediction. |
| format | Article |
| id | doaj-art-8f4fed1c779c46808f5d4aa83b362c6f |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-8f4fed1c779c46808f5d4aa83b362c6f2025-01-30T05:15:19ZengElsevierSystems and Soft Computing2772-94192025-12-017200192Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddingsBiplov Paneru0Bipul Thapa1Bishwash Paneru2Department of Electronics and Communication Engineering, Nepal Engineering College, Affiliated to Pokhara University, Bhaktapur, Nepal; Corresponding author.Department of Computer Science and Engineering, Kathmandu University, Kavre, Dhulikhel, NepalDepartment of Applied Sciences and Chemical Engineering, Institute of Engineering, Tribhuvan University, NepalSentiment analysis, an important task in Natural Language Processing (NLP), focuses on identifying and extracting sentiments from input. With the exponential expansion of digital information, sentiment analysis has recently gained significant attention across various domains. Traditional sentiment analysis methods paired with static embeddings often fall short in capturing the deep contextual relationships within text. In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). We provide a comprehensive overview of the creation and assessment of a convolutional learning model especially suited for sentiment analysis of movie reviews using a dataset of around 50k entries. The proposed approach preprocesses movie reviews, employs RoBERTa to generate rich contextual embeddings, and processes these embeddings through a simple yet effective R-CNN architecture. We perform comprehensive analysis of the R-CNN model, showing a superior test accuracy of 91.5 %, achieving the best results compared to the baseline. Additionally, we develop a Flask-based application, demonstrating the practical applicability of our R-CNN model for real-time sentiment prediction.http://www.sciencedirect.com/science/article/pii/S2772941925000109Natural language processing (NLP)Sentiment analysisRoBERTaMovie reviewsConvolutional neural network (CNN)Flask application |
| spellingShingle | Biplov Paneru Bipul Thapa Bishwash Paneru Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings Systems and Soft Computing Natural language processing (NLP) Sentiment analysis RoBERTa Movie reviews Convolutional neural network (CNN) Flask application |
| title | Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings |
| title_full | Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings |
| title_fullStr | Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings |
| title_full_unstemmed | Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings |
| title_short | Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings |
| title_sort | sentiment analysis of movie reviews a flask application using cnn with roberta embeddings |
| topic | Natural language processing (NLP) Sentiment analysis RoBERTa Movie reviews Convolutional neural network (CNN) Flask application |
| url | http://www.sciencedirect.com/science/article/pii/S2772941925000109 |
| work_keys_str_mv | AT biplovpaneru sentimentanalysisofmoviereviewsaflaskapplicationusingcnnwithrobertaembeddings AT bipulthapa sentimentanalysisofmoviereviewsaflaskapplicationusingcnnwithrobertaembeddings AT bishwashpaneru sentimentanalysisofmoviereviewsaflaskapplicationusingcnnwithrobertaembeddings |