ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Syamala, Maganti; Nalini, N

doi:10.18421/tem101-11

Cited by 3 publications

(2 citation statements)

References 21 publications

(22 reference statements)

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“…Feature level fusion was applied by proposing a NWAE mechanism as a feature selection measure to overcome the problem of dimensionality. The obtained results obtained from speech and three variants of text analysis models are compared with the individual text feature extraction techniques [18] [19] [20] and proved that the proposed hybrid level fusion mechanism improves the user readability by faster access and there by improves the performance.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Aspect based Sentiment Analysis Model by the Hybrid Fusion of Speech and Text Aspects

Syamala¹,

Nalini²

2021

IJACSA

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Aspect-based Sentiment Analysis (ABSA) is treated to be a challenging task in the domain of speech, as it needs the fusion of acoustic features and Linguistic features for information retrieval and decision making. The existing studies in speech are limited to speech and emotion recognition. The main objective of this work is to combine acoustic features in speech with linguistic features in text for ABSA. A deep learning and language model is implemented for acoustic feature extraction in speech. Different variants of text feature extraction techniques are used for aspect extraction in text. Trained Lexicons, Latent Dirichlet Allocation (LDA) model, Rule based approach and Efficient Named Entity Recognition (E-NER) guided dependency parsing approach has been used for aspect extraction. Sentiment with respect to the extracted aspect is analyzed using Natural Language Processing (NLP) techniques. The experimental results of the proposed model proved the effectiveness of hybrid level fusion by yielding improved results of 5.7% WER and 3% CER when compared with the traditional baseline individual linguistic and acoustic feature models.

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Section: Discussionmentioning

confidence: 99%

An Efficient Aspect based Sentiment Analysis Model by the Hybrid Fusion of Speech and Text Aspects

Syamala¹,

Nalini²

2021

IJACSA

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“…Another research [11] effort in the same year focused on incorporating emotional affects into aspect extraction through a novel supervised approach, providing more comprehensive information for decision-making. Furthermore, a domain-independent dynamic ABSA model [12] was introduced in 2021, automating the aspect extraction and sentiment analysis process using Efficient Named Entity Recognition (E-NER) guided dependency parsing and Neural Networks (NN). In 2022 [13], efforts were made to enhance aspect-based sentiment analysis for reviews in the Indonesian language through a deep learning approach in semi-supervised graph-based, such as GCN and GRN for aspect and opinion relationships detection, while polarity classification in CNN and RNN demonstrating superior performance over existing models.…”

Section: Related Workmentioning

confidence: 99%

Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA

Akram,

Sabir

2023

IJCAI

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Aspect extraction plays a crucial role in understanding the fine-grained nuances of text data, allowing businesses and researchers to gain deeper insights into customer opinions, sentiment distributions, and preferences. This study presents a BERT-based framework for aspect extraction in ABSA and evaluates its performance. Our research focuses on the comprehensive analysis of aspect extraction as we test our method using the SEMEVAL dataset of various consumer evaluations across diverse domains, including laptops, restaurants, and Twitter. By fine-tuning BERT on a large dataset, we aim to overcome the limitations of traditional approaches and improve the accuracy and efficiency of aspect extraction in ABSA. The experimental findings provide evidence of the efficacy of our methodology with a noteworthy aspect extraction accuracy of 0.98, highlighting its capacity to properly and consistently extract features. The article also explores the applicability of our approach to new domains and its possible applications in real-world scenarios.Povzetek: Ta študija predstavlja okvir za ugotavljanje aspekta v ABSA, ki temelji na sistemu BERT.

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Exploring fetal brain tumor glioblastoma symptom verification with self organizing maps and vulnerability data analysis

Natarajan,

Mathivanan

et al. 2024

Sci Rep

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Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI “Magnetic Resonance Imaging” brain image using a powerful magnetic field, radio waves, and a computer to produce detailed images of the body's internal structures it is a standard diagnostic tool for a wide range of medical conditions, from detecting brain and spinal cord injuries to identifying tumors and also in evaluating joint problems. This is treatable, and by enabling the factor for happening, the factor for dissolving the dead tissues. If the brain tumor glioblastoma is untreated, the child will go to death; to avoid this, the child has to treat the brain problem using the scan of MRI images. Using the neural network, brain-related difficulties have to be resolved. It is identified to make the diagnosis of glioblastoma. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. The process of maximum and min rationalization is used to recognize the Brain tumor glioblastoma in the brain images for treatment efficiency. The image segment is created for image recognition. The method of boosted division time attribute extraction is used in image recognition with the help of MRI for image extraction. The proposed boosted division time attribute extraction method helps to recognize the fetal images and find Brain tumor glioblastoma with feasible accuracy using image rationalization against the brain tumor glioblastoma diagnosis. In addition, 45% of adults are affected by the tumor, 40% of children and 5% are in death situations. To reduce this ratio, in this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis. Then the tumor grades were analyzed using the efficient method for the imaging MRI with the diagnosis result of partially high. The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively. Experimental results show that the proposed method archives rate of 98.12% accuracy with low response time and compared with the Genetic algorithm (GA), Convolutional Neural Network (CNN), fuzzy-based minimum and maximum neural network (Fuzzy min–max NN), and kernel-based support vector machine. Specifically, the proposed method achieves a substantial improvement of 80.82%, 82.13%, 85.61%, and 87.03% compared to GA, CNN, Fuzzy min–max NN, and kernel-based support vector machine, respectively.

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ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Cited by 3 publications

References 21 publications

An Efficient Aspect based Sentiment Analysis Model by the Hybrid Fusion of Speech and Text Aspects

An Efficient Aspect based Sentiment Analysis Model by the Hybrid Fusion of Speech and Text Aspects

Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA

Exploring fetal brain tumor glioblastoma symptom verification with self organizing maps and vulnerability data analysis

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