Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

Sivakumar, S.; Rajalakshmi, R.

doi:10.1111/coin.12400

Cited by 22 publications

(7 citation statements)

References 58 publications

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“…The Table II presents a comparative analysis of different methods based on their performance metrics, including accuracy, precision, recall, and F1-score. Each row corresponds to a specific method, such as SNN & CNN [17] , Logistic Regression [18], CBRNN [19], and the proposed GRU-BERT model. Accuracy refers to the overall correctness of the model's predictions, while precision measures the ratio of correctly predicted positive cases to the total predicted positive cases.…”

Section: A Performance Evaluationmentioning

confidence: 99%

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

Jayakumar,

Rajakumari,

Sarwar

et al. 2024

IJACSA

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Semantics describe how language and its constituent parts are understood or interpreted. Semantic analysis is the computer analysis of language to derive connections, meaning, and context from words and sentences. In English language learning, dynamic content generation entails developing instructional materials that adjust to the specific requirements of each student, delivering individualized and contextually appropriate information to boost understanding and engagement. To tailor instructional materials to the various requirements of students, dynamic content creation is essential in English language learning (ELL). This work is a unique method for automatic and adaptive content production in ELL that uses Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) together. The suggested approach uses BERT enabling content selection, adaption, and adaptive educational content production, and GRU for semantic extraction of features and contextual information captured from textual input. The article presents a novel approach to creating automated and adaptable educational tools for ELL that uses GRU for semantic feature extraction. Using persuasive essays collected in the PERSUADE 2.0 corpus annotated with discourse components and competency scores, this is an extensive dataset. After extensive testing, this approach shows outstanding outcomes, with high accuracy reaching 97% when compared to the current Spiking Neural Network (SNN) &Convolutional Neural Network (CNN), Logistic Regression (LR), and Convolutional Bidirectional Recurrent Neural Network (CBRNN). Python is used to implement the suggested work. The suggested strategy improves ELL engagement and understanding by providing individualized, contextually appropriate learning resources to each student. In addition, the flexibility of the system allows for real-time modifications to suit the changing needs and preferences of the learners. By providing instructors and students in a variety of educational contexts with a scalable and effective approach, this study advances automated content development in ELL. The model architecture will be improved in the future, along with the application's expansion into other domains outside of ELL and the investigation of new language aspects.

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Section: A Performance Evaluationmentioning

confidence: 99%

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

Jayakumar,

Rajakumari,

Sarwar

et al. 2024

IJACSA

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“…According to the authors, Bidirectional LSTM (Bi-LSTM) showed better results as compared to CNN, LSTM, CNN-LSTM, GRU-LSTM, and other machine learning algorithms namely SVM, Logistic Regression (LR), and MNB. Soubraylu and Rajalakshmi ( 2021 ) proposed a hybrid convolutional bidirectional recurrent neural network, where the rich set of phrase-level features are extracted by the CNN layer and the chronological features are extracted by Bidirectional Gated Recurrent Unit (BGRU) through long-term dependency in a multi-layered sentence. Priyadarshini and Cotton ( 2021 ) suggested a sentiment analysis model using LSTM-CNN for a fully connected deep neural network and a grid search strategy for hyperparameter tuning optimization.…”

Section: General Framework Of Sentiment Analysismentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

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“…When conducting sentiment analysis, rather than focusing on applying word-level features, Soubraylu & Rajalakshmi (2021) considered phrase-level and sentence-level features instead. Additionally, they improved results by employing a variety of deep learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Angamuthu,

Trojovský

2023

PeerJ Computer Science

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Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users’ preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users’ opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using ‘TripAdvisor reviews’, ‘TMDB 5000 movies’, and an ‘Amazon dataset’. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

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Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

Cited by 22 publications

References 58 publications

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

Sentiment analysis: A survey on design framework, applications and future scopes

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

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