Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews
- Title
- Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews
- Creator
- Kapruwan A.; Pandey P.; Tripathi A.P.; Dhaundiyal P.; Patil H.; Maranan R.
- Description
- Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. 2024 IEEE.
- Source
- 4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings, pp. 651-657.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bag-Of-Words and Term Frequency-Inverse Document Frequency; Bowerbird Optimization Algorithm; Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network; Sentiment Analysis
- Coverage
- Kapruwan A., Graphic Era Deemed to be University, Department of Computer Science & Engineering, Uttarakhand, Dehradun, 248002, India; Pandey P., Akgec Ghaziabad, Department of Mca, Uttar Pradesh, Ghaziabad, 201001, India; Tripathi A.P., School of Business & Management (SBM), Christ (Deemed to be University), Delhi NCR campus, Uttar Pradesh, Ghaziabad, 201003, India; Dhaundiyal P., School of Business & Management (SBM), Christ (Deemed to be University), Delhi NCR campus, Uttar Pradesh, Ghaziabad, 201003, India; Patil H., School of Computer Science & Engineering, Iilm University, Uttar Pradesh, Greater Noida, 201306, India; Maranan R., Saveetha School of Engineering, Simats, Department of Research and Innovation, Tamil Nadu, Chennai, 602105, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-833154036-4
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kapruwan A.; Pandey P.; Tripathi A.P.; Dhaundiyal P.; Patil H.; Maranan R., “Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews,” CHRIST (Deemed To Be University) Institutional Repository, accessed May 13, 2025, https://archives.christuniversity.in/items/show/18994.