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61
Local Sequence Alignment Experiment (3) ...
Gagniuc, Paul A.. - : figshare, 2022
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62
Is implicit information quantifiable? A corpus-based analysis of British and Italian political tweets. ...
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63
Illocutionary Function Of Language In The Healing Miracles Of Jesus Christ.pdf ...
Ellah, Stephen Magor. - : figshare, 2022
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64
Illocutionary Function Of Language In The Healing Miracles Of Jesus Christ.pdf ...
Ellah, Stephen Magor. - : figshare, 2022
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65
Local Sequence Alignment Experiment (3) ...
Gagniuc, Paul A.. - : figshare, 2022
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66
Is implicit information quantifiable? A corpus-based analysis of British and Italian political tweets. ...
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67
A discourse study of selected newspaper headlines on insurgency in Nigeria ...
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68
Age of Acquisition and Spoken Words: Examining Hemispheric Differences in Lexical Processing ...
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69
Intraspeaker Priming across the New Zealand English Short Front Vowel Shift ...
Villarreal, Dan; Clark, Lynn. - : SAGE Journals, 2022
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70
Age of Acquisition and Spoken Words: Examining Hemispheric Differences in Lexical Processing ...
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71
Intraspeaker Priming across the New Zealand English Short Front Vowel Shift ...
Villarreal, Dan; Clark, Lynn. - : SAGE Journals, 2022
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72
sj-pdf-1-las-10.1177_00238309211068402 – Supplemental material for Age of Acquisition and Spoken Words: Examining Hemispheric Differences in Lexical Processing ...
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73
sj-pdf-1-las-10.1177_00238309211068402 – Supplemental material for Age of Acquisition and Spoken Words: Examining Hemispheric Differences in Lexical Processing ...
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74
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
Abstract: The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.
Keyword: Arabic NLP; Arabic social media; lexical; Saudi dialects; sentiment analysis; Twitter
URL: https://doi.org/10.3390/app12083806
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75
Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis: The Case of the Flood Disaster in the Yangtze River Basin in China in 2020
In: Remote Sensing; Volume 14; Issue 5; Pages: 1199 (2022)
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76
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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77
Social Media and the Pandemic: Consumption Habits of the Spanish Population before and during the COVID-19 Lockdown
In: Sustainability; Volume 14; Issue 9; Pages: 5490 (2022)
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78
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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79
Artificial Intelligent in Education
In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
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80
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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