Semantic Ontology-Based Approach to Enhance Arabic Text Classification

Hawalah, Ahmad

doi:10.3390/bdcc3040053

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…Hawalah A [3] presented the enhanced Arabic topic-discovery architecture (EATA), which provides a semantic ontology-based approach to enhance Arabic text classification. The lack of studies in the Information retrieval domain for the Arabic language has motivated us to present a novel approach in improving the traditional classification algorithms to cope with the challenges in processing the Arabic language.…”

Section: Literature Surveymentioning

confidence: 99%

OntoImVid-ontology based semantic image/video processing

Lakshmi

Sankari

2022

ijhs

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The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. A low-level features and video content analysis algorithms are integrated into the ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how features and algorithms for video analysis should be applied according to different perception content and low-level features. OntoImVid structure is created from both image and video semantic analysis results. Proposed OntoImVid combines the image and Video retrieval in same semantic retrieval technique in efficient manner.

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Section: Literature Surveymentioning

confidence: 99%

OntoImVid-ontology based semantic image/video processing

Lakshmi

Sankari

2022

ijhs

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“…Most of the work performed with tweets from Saudi Arabia has focused on sentiment and semantic analysis [22,23]. Words were labeled based on their polarity.…”

Section: Related Workmentioning

confidence: 99%

Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

et al. 2021

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Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion that was dominant or most expressed in both places. However, a larger proportion of users living in Mecca fell under this category. The proposed analysis was an initial attempt toward studying the emotional and behavioral differences between users living in different cities of Saudi Arabia. This study has several other important applications. First, the emotion-based study could contribute to the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a particular user.

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“…Along with English language, many researches on other languages like-Spanish [8], Arabic [7], Chinese [9] is occurred and still going on. Ahmad explores arabic text classification based on semantic ontology [7]. Term frequencyinverse document frequency (TF-IDF) is performed on arabic text documents.…”

Section: Fig1 General Approach Of Bengali Text Classificationmentioning

confidence: 99%

Bengali News Headline Categorization Using Optimized Machine Learning Pipeline

Dhar¹,

Abedin²

2021

IJIEEB

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Bengali text based news portal is now very common and increasing day by day. With easy access of internet technology, reading news through online is now a regular task. Different types of news are represented in the news portal. The system presented in this paper categorizes the news headline of news portal or sites. Prediction is made by machine learning algorithm. Large number of collected data are trained and tested. As pre-processing tasks such as tokenization, digit removal, removing punctuation marks, symbols, and deletion of stop words are processed. A set of stop words is also created manually. Strong stop words leads to better performance. Stop words deletion plays a lead role in feature selection. For optimization, genetic algorithm is used which results in reduced feature size. A comparison is also explored without optimization process. Dataset is established by collecting news headline from various Bengali news portal and sites. Resultant output shows well performance in categorization.

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Semantic Ontology-Based Approach to Enhance Arabic Text Classification

Cited by 12 publications

References 35 publications

OntoImVid-ontology based semantic image/video processing

OntoImVid-ontology based semantic image/video processing

Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

Bengali News Headline Categorization Using Optimized Machine Learning Pipeline

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