An automated essay evaluation system using natural language processing and sentiment analysi

Sadanand, Vijaya Shetty; Guruvyas, Kadagathur Raghavendra Rao; Patil, Pranav Prashantha; Acharya, Jeevan Janardhan; Sharvani, G. S.

doi:10.11591/ijece.v12i6.pp6585-6593

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…The primary goals of the study reported in [42] are the proposal and training of a long-short-term memory (LSTM) model on a collection of hand evaluated essays with scores. The purpose of sentiment analysis is to establish whether an article is positive, negative, or neutral.…”

Section: Related Workmentioning

confidence: 99%

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

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Section: Related Workmentioning

confidence: 99%

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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“…CNN is an example of a feedforward network, in which information only flows in a single direction, from input to output [28], [29]. Even though there are several CNN architectures, most CNNs have convolutional and pooling layers.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based techniques and error level analysis for image tamper detection

Shetty Sadanand,

Shetty Janardhana,

Purushothaman

et al. 2024

IJEECS

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<span>Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.</span>

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“…The ontology of ATP modern methods includes both classical ATP methods and methods using machine learning. The papers [19]- [21] existing ontologies containing ATP methods were analyzed. At the moment, there is an ontology of machine learning [22], [23] which contains a small set of ATP methods based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Ontology engineering of automatic text processing methods

Sadirmekova,

Tussupov,

Murzakhmetov

et al. 2023

IJECE

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<span lang="EN-US">Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.</span>

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An automated essay evaluation system using natural language processing and sentiment analysi

Cited by 8 publications

References 22 publications

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

Convolutional neural network-based techniques and error level analysis for image tamper detection

Ontology engineering of automatic text processing methods

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