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How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
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In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
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| 2 |
Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020
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| 3 |
Dynamics of prescriptivism and lexical borrowings in Contemporary French
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| 4 |
Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data
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In: Sensors; Volume 22; Issue 1; Pages: 155 (2021)
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| 5 |
A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media
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In: Information ; Volume 12 ; Issue 8 (2021)
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| 6 |
This! Identifying new sentiment slang through orthographic pleonasm online: Yasss slay gorg queen ilysm
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In: 36 ; 4 ; 114 ; 120 (2021)
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| 7 |
An Enhanced Corpus for Arabic Newspapers Comments
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In: ISSN: 1683-3198 ; International Arab Journal of Information Technology ; https://hal.archives-ouvertes.fr/hal-03124728 ; International Arab Journal of Information Technology, Colleges of Computing and Information Society (CCIS), 2020, 17 (5), pp.789-798. ⟨10.34028/iajit/17/5/12⟩ (2020)
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| 8 |
Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication
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In: Applied Sciences ; Volume 10 ; Issue 2 (2020)
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| 9 |
Portuguese Comparative Sentences: A Collection of Labeled Sentences on Twitter and Buscapé ...
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| 10 |
Portuguese Comparative Sentences: A Collection of Labeled Sentences on Twitter and Buscapé ...
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| 11 |
Techniques for improving the labelling process of sentiment analysis in the Saudi stock market
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Abstract:
Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" (Arabic source), "losses" (Arabic source), "green colour" (Arabic source:Arabic source), "growing" (Arabic source), "distribution" (Arabic source), "decrease" (Arabic source), "financial penalty" (Arabic source), and "delay" (Arabic source). Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%. ; open access
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Keyword:
Arabic; Arabic language; association rule; opinion mining; sentiment analysis; Twitter
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URL: https://doi.org/10.14569/IJACSA.2018.090307
http://hdl.handle.net/10547/623813
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| 12 |
Analysis of the relationship between Saudi twitter posts and the Saudi stock market
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| 13 |
Identifying Mubasher software products through sentiment analysis of Arabic tweets
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| 14 |
Visualising Arabic sentiments and association rules in financial text
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| 15 |
Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions
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In: Information ; Volume 10 ; Issue 1 (2019)
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| 16 |
Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany
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In: Sustainability ; Volume 11 ; Issue 19 (2019)
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| 17 |
Text mining with word embedding for outlier and sentiment analysis
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| 18 |
Cross-Cultural Examination on Content Bias and Helpfulness of Online Reviews: Sentiment Balance at the Aspect Level for a Subjective Good
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| 20 |
Analyzing Public Outlook towards Vaccination using Twitter
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In: Computer Science and Engineering Faculty Publications (2019)
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