Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network

Mutinda, James; Mwangi, Waweru; Okeyo, George

doi:10.3390/app13031445

Cited by 60 publications

(10 citation statements)

References 39 publications

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“…It is notable that the proposed BERT-384-32 model outperformed the other models with the highest accuracy. The proposed BERT-384-32 model (97.3% accuracy) is followed by the proposed BERT-320-32 model (96.9%), and then LoBERT (BERT+ CNN) proposed by Mutinda et al [20]. After that follows the BERT model proposed by Mutinda et al [20] (84.00% accuracy) and the BERT-320-32 proposed by Bilal and Almazroi [6] (71.7% accuracy).…”

Section: Resultsmentioning

confidence: 96%

“…The proposed BERT-384-32 model (97.3% accuracy) is followed by the proposed BERT-320-32 model (96.9%), and then LoBERT (BERT+ CNN) proposed by Mutinda et al [20]. After that follows the BERT model proposed by Mutinda et al [20] (84.00% accuracy) and the BERT-320-32 proposed by Bilal and Almazroi [6] (71.7% accuracy). It is worth nothing that despite the better accuracy produced by SVM as compared to KNN and NB, SVM performs efficiently, which conforms to the finding in [31].…”

Section: Resultsmentioning

confidence: 96%

“…Ge et al's [19] proposal for recommending reviews was to have the model predict how useful a review would be based on the ratings given to those reviews that had already received positive ratings. Mutinda et al [20] suggested the LEBERT model, which employs an n-gram to segment the input text and a sentiment lexicon to determine whether segments of the text contain sentimental words. BERT then uses these detected sections' words to create a vector.…”

Section: Literature Reviewmentioning

confidence: 99%

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Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model

Areshey

Mathkour

2023

Sensors

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Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

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Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model

Areshey

Mathkour

2023

Sensors

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“…James Mutinda et al (2023) -The study used the LeBERT model to conduct experiments on reviews from Yelp, IMDB, and Amazon. LeBERT employed a Convolutional Neural Network (CNN) for classification and BERT word embeddings, sentiment lexicons, and N-grams for text vectorization.…”

Section: Literature Surveymentioning

confidence: 99%

Decoding Human Emotions Through Multimodal Analysis Using Deep Learning

2024

IJSREM

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In the evolving landscape of emotion recognition, this study addresses the inherent shortcomings in existing technology. Recognizing the imperative role emotions play in human interaction, the need for a more nuanced and accurate multimodal emotion recognition system is evident. This research aims to refine multimodal emotion recognition by addressing existing challenges. The problem statement revolves around enhancing the accuracy and depth of emotion recognition through innovative methodologies. Leveraging Convolutional Neural Networks (CNN), Attention layers, and Bi-LSTM networks, the study utilizes a comprehensive approach. Data fusion involves combining audio extracted from video data, followed by fusion combinations of audio and text. The methodology incorporates SoftMax classifiers for feature recognition. The fusion of audio, text, and video data, coupled with the innovative use of CNN-LSTM and Attention layer networks, contributes to this success. The discussion interprets these results, highlighting the efficacy of the proposed approach in addressing the challenges of multimodal emotion recognition. The outcomes signify a substantial advancement in the field, providing a more comprehensive understanding of emotional expressions.

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“…The aforementioned sentiment analysis is one of the important components in natural language processing (NLP): it is primarily about determining the degree of expressiveness of the text under research, which was the object of research in the work of Mutinda J., Mwangi W., Okeyo G. [40]. The researchers emphasize that there are two groups of text representation methods: lexicon-based and Machine Learning.…”

Section: Neural Network Modeling As a Tool For Analyzing Language Unitsmentioning

confidence: 99%

Analysis of the Theoretical Foundations of Neural Network Modeling of Language Unit Recognition

Dovhan¹

2023

Scientific Space: Integration of Traditional and Innovative Processes

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In the context of linguistic science and the integration of the mathematical paradigm into humanitarian discourse, the analysis and processing of natural language (in particular, neural network modeling of language categories) is an important and urgent task. Research by a number of authors and a series of experiments show that artificial neural networks of various types and kinds with different parameterizations can significantly optimize linguistic research: accelerate, deepen, integrate into various scientific fields, etc. At the same time, the use of artificial neural networks in linguistics is an important area of work, as well as a powerful and productive tool for a number of relevant studies, which, however, requires careful analysis and development of implementation strategies. The purpose of the article is to analyze the features of neural network modeling of language units recognition as an effective method of cognition within the anthropocentric paradigm of research. The solution of such research tasks determines the logic of presentation of the studied material in the article: introduction, systematization of achievements in the theory and practice of modeling as a universal tool of scientific cognition in general and theoretical justification of neural network modeling in the context of linguistic paradigm. Methodology of the study is based of the method of analyzing scientific research was updated, which led to the search and analysis of scientific publications related to neural network modeling (in particular, language units). We analyzed more than 60 recent scientific studies and publications covering aspects of the problem under study, which we evaluated based on their relevance, methodological specificity, and scientific novelty. Thus, the research methods outlined in this article allowed us to conduct a thorough analysis of the state and prospects for the development of the theoretical foundations of neural network modeling of language unit recognition. The analysis of the latest scientific research and publications has made it possible to determine the role and place of artificial neural networks of various types and their specifications in the process of modeling language units. The above made it possible to identify the main trends in working with text data aimed at improving the quality of their processing, generation, etc. Results of the survey showed that one of the core tasks of modern linguistic science is to understand the language polysystem, the peculiarities of its structure and the nature of its functioning. In addition, the discursive nature of language practices in the context of data interconnection is also important for documenting language structures and verbal practices that are socially determined. That is why the use of neural networks as a tool for conducting local linguistic and integrated scientific research involving the mathematical paradigm is gaining popularity. Neural network modeling of language categories in this context is the basic basis for such research. Practical implications. The actualization of neural network modeling of linguistic categories is not only one of the means of studying linguistic polysystems, but also an objective criterion of checking the truth of linguistic knowledge. Value/originality. Neural network modeling of language unit recognition has a significant impact on the development, improvement, and evolution of modern linguistic research. The effectiveness of neural network modeling of language categories creates opportunities for a deeper understanding of the studied linguistic objects, phenomena and processes, encouraging linguists to develop various linguistic models that could solve practical linguistic problems (information retrieval, machine translation, natural language processing, knowledge extraction and localization from text, etc.).

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Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network

Cited by 60 publications

References 39 publications

Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model

Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model

Decoding Human Emotions Through Multimodal Analysis Using Deep Learning

Analysis of the Theoretical Foundations of Neural Network Modeling of Language Unit Recognition

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