NLP based Deep Learning Approach for Plagiarism Detection

Rosu, Razvan; Stoica, Alexandru Stefan; Popescu, Paul Ștefan; Mihăescu, Marian Cristian

doi:10.37789/ijusi.2020.13.1.4

Cited by 4 publications

(3 citation statements)

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“…In Ref. [13], the authors have compared the performances of Global Vectors for word representation(GloVe), Bidirectional Encoder Representations from Transformers(BERT) and Robustly Optimized BERT(RoBERTa) in detecting plagiarism in articles and have concluded that BERT has outperformed the other two methods. The authors of Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Text embedding techniques for efficient clustering of twitter data

Ravi

Kulkarni

2023

Evol. Intel.

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World wide web is abundant with various types of information such blogs, social media posts, news articles. With this type of magnitude of online content, there is a need to deeply understand the insights of it in order to make use of the information for practical applications such as event detection, polarity, sentiment analysis and so on. Natural Language Processing (NLP) is the study of such information which is used for text classification, sentiment analysis, clustering of similar text. NLP makes use of linguistic knowledge and build machine learning models to analyse textual information. NLP finds its way in various applications like classification of online review into positive and negative without actually reading the reviews and feedback. For text analysis, there should be a way to quantify the text based on its frequency of occurrence, correlation with neighbouring words, contextual similarity of words, etc. One such way is word embedding. This study applies various word embedding techniques on tweets of popular news channels and clusters the resultant vectors using K-means algorithm. From this study, it is found out that Bidirectional Encoder Representations from Transformers (BERT) has achieved highest accuracy rate when used with K-means clustering.

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Section: Literature Reviewmentioning

confidence: 99%

Text embedding techniques for efficient clustering of twitter data

Ravi

Kulkarni

2023

Evol. Intel.

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“…Their work focuses on polarity, but research into assigning emotions to items stated in text is also a viable subject for future research. [15]. Sautera et al explored six emotions, which have been the subject of a lot of recent research.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis of Omicron Tweets by using Machine Learning Models

Fazal,

Khan,

Maqbool

et al. 2023

VFAST trans. softw. eng.

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The COVID-19 epidemic has been affecting a lot of individuals worldwide since 2019. It is emerging as an infectious disease that set off a disaster with far-reaching effects on things like education, economics, and health. During the coronavirus outbreak, new COVID-19 mutations such the Beta, Delta, and Omicron variants emerged, terrifying and alarmed the population. Around 6 million people reportedly died as a result of COVID-19 variations, according to World Meter. The SARS-CoV-2 omicron strain was initially identified in South Africa on November 24, 2021, and it has since spread to more than 57 nations. In this essay, we examine how people feel and act toward the omicron variation. On Omicron, we proposed an approach for determining sentiment analysis for tweets from Twitter. The analysis of Twitter data's sentiment has a lot of potential. In the intended methodology, we extract the best characteristics from the Omicron tweets using NLP techniques in Python, resulting in a dataset that can be used to train the Models. The produced dataset was employed by four ML Classifiers, including “Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)”, to accurately categorise users' emotional behavior into three categories: neutral, negative, and positive. The Class Neutral receives the best score and the Class Negative receives the lowest score based on the accuracy of the forecast level.

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“…Regarding scientific research articles it could be categorize plagiarism into (Kharat, Chavan, Jadhav, & Rakibe, 2013;Rosu, Stoica, Popescu, & Mihăescu, 2021): a) Using a source's keywords, words, sentences, facts, and/or information without citing the source in the citation notes or referencing the source appropriately. b) Referring to and/or randomly quoting keywords, words, sentences, data, and/or information from a source without citing the source and/or without completely identifying the source in the citation notes c) Citing a source for ideas, opinions, points of view, or hypotheses without properly citing the source.…”

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confidence: 99%

A proposed approach for plagiarism detection in Article documents

Saeed

Taqa

2022

SinkrOn

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According to the scientific institutes, Plagiarism is defined as claiming someone else's ideas or efforts as one's own without citing the sources. Systems of plagiarism detection typically use a text similarity algorithm in a text document to look for common sentences between source and suspicious documents, either by directly matching the sentences or by embedding the sentences into a vector using TFIDF-like or other methods and then calculating the distance or the similarity between the source and suspect sentence vectors. The cosine similarity method is one of the methods for determining that distance. To cluster the documents and choose only related documents for detection, an unsupervised Machine learning technique such as K-means could be utilized. In this paper, a plagiarism detecting application was created and tested on many text document types, including doc, Docx, and pdf of research papers that were collected from the web to build the source corpus. To calculate the level of similarity between the suspicious article and the corpus of source articles, the TFIDF text encoding approach is used with NLP, K-means clustering, and cosine similarity algorithms. The proposed application was carried out with five different documents and resulted in different ratios of plagiarism, the first document has a 0.27 ratio, the second document has a 0.15 ratio, the third document has 0.19 ratio while document 4 has a 0.42 ratio, and finally, document 5 has 0.37 ratio of plagiarism. The generated detailed plagiarism ratio report presents the percentage of plagiarism in the suspicious article document. Depending on the threshold value, the application will decide if the suspicious document is acceptable or not.

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NLP based Deep Learning Approach for Plagiarism Detection

Cited by 4 publications

References 10 publications

Text embedding techniques for efficient clustering of twitter data

Text embedding techniques for efficient clustering of twitter data

Sentiment Analysis of Omicron Tweets by using Machine Learning Models

A proposed approach for plagiarism detection in Article documents

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