Cloud-based Textual Analysis as a Basis for Document Classification

Weir, George R. S.; Owoeye, Kolade Olawande; Oberacker, Alice; Alshahrani, Haya Mesfer

doi:10.1109/hpcs.2018.00110

Cited by 8 publications

(4 citation statements)

References 15 publications

(29 reference statements)

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“…Once the mining activity was completed and to strengthen the pattern detection process, the findings were complemented with three additional techniques: linguistic analysis [70], textual analysis [71], and experimental analysis [72] for each of the four main concepts established in this study.…”

Section: Results Stagementioning

confidence: 99%

Discovery Model Based on Analogies for Teaching Computer Programming

2021

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Teaching the fundamentals of computer programming in a first course (CS1) is a complex activity for the professor and is also a challenge for them. Nowadays, there are several teaching strategies for dealing with a CS1 at the university, one of which is the use of analogies to support the abstraction process that a student needs to carry for the appropriation of fundamental concepts. This article presents the results of applying a discovery model that allowed for the extraction of patterns, linguistic analysis, textual analytics, and linked data when using analogies for teaching the fundamental concepts of programming by professors in a CS1 in university programs that train software developers. For that reason, a discovery model based on machine learning and text mining was proposed using natural language processing techniques for semantic vector space modeling, distributional semantics, and the generation of synthetic data. The discovery process was carried out using nine supervised learning methods, three unsupervised learning methods, and one semi-supervised learning method involving linguistic analysis techniques, text analytics, and linked data. The main findings showed that professors include keywords, which are part of the technical computer terminology, in the form of verbs in the statement of the analogy and combine them in quantitative contexts with neutral or positive phrases, where numerical examples, cooking recipes, and games were the most used categories. Finally, a structure is proposed for the construction of analogies to teach programming concepts and this was validated by the professors and students.

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Section: Results Stagementioning

confidence: 99%

Discovery Model Based on Analogies for Teaching Computer Programming

2021

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“…With regard to current research, for example, it is more convenient to focus on certain deceptive markers in a text to predict the content's veracity than to attempt to analyze the whole text to try to find nuanced differences between it and a non-fake text. In fact, textual analysis with the aid of NLP and ML has been employed in research to detect fake news [35], [36], [37].…”

Section: Textual Analysismentioning

confidence: 99%

Tasaheel: An Arabic Automative Textual Analysis Tool—All in One

Himdi,

Assiri

2023

IEEE Access

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This paper demonstrates Tasaheel, an automative Arabic textual analysis tool. It offers two types of textual analysis utilities: traditional natural language processing tasks; such as stemming, segmentation, normalization, name entity recognition, and part of speech tagging, by integrating open-sourced Arabic natural language processing packages. The second type of utility offers novel corpus analysis methods, including a detailed word use summary of part of speech, emotion, polarity, linguistics, and domain-specific word labeling. Tasaheel is the first Arabic tool that analyses affixes as a type of comprehensive textual analysis, along with options to search and handle data. We anticipate that Tasaheel will be a conducive tool for Arabic textual analysis. INDEX TERMSNLP, text analysis, tool, development.

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“…Offensive comments such as HS and cyberbullying are the most researched areas in NLP in the past few decades [21]. ML algorithms have been of great help in this direction in terms of SM data analysis for the identification and classification of offensive comments [22].…”

Section: B Related Workmentioning

confidence: 99%

Advances in Machine Learning Algorithms for Hate Speech Detection in Social Media: A Review

Mullah

Zainon

2021

IEEE Access

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The aim of this paper is to review machine learning (ML) algorith ms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline componentsdata collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are two-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.

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Cloud-based Textual Analysis as a Basis for Document Classification

Cited by 8 publications

References 15 publications

Discovery Model Based on Analogies for Teaching Computer Programming

Discovery Model Based on Analogies for Teaching Computer Programming

Tasaheel: An Arabic Automative Textual Analysis Tool—All in One

Advances in Machine Learning Algorithms for Hate Speech Detection in Social Media: A Review

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