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Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms
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In: Information; Volume 13; Issue 3; Pages: 152 (2022)
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Gegen die Öffentlichkeit: Alternative Nachrichtenmedien im deutschsprachigen Raum
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Schwaiger, Lisa. - : transcript Verlag, 2022. : DEU, 2022. : Bielefeld, 2022
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In: 46 ; Digitale Gesellschaft ; 327 (2022)
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Cuando la negatividad es el combustible. Bots y polarización política en el debate sobre el COVID-19
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In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 71, 2022 (Ejemplar dedicado a: Discursos de odio en comunicación: Investigaciones y propuestas), pags. 63-75 (2022)
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Sentiment Analysis of Arabic Documents
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In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
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Aspect Level Public Opinion Detection, Tracking and Visualization on Social Media ...
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On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu ...
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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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| 8 |
Analyzing Tweets to Rank FIFA Players using Named Entity Recognition ...
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| 9 |
Analyzing Tweets to Rank FIFA Players using Named Entity Recognition ...
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| 10 |
Assessment of News Items Objectivity in Mass Media of Countries with Intelligence Systems: the Brexit Case
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In: Media Watch ; 10 ; 3 ; 471-483 (2021)
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| 11 |
Discourse Networks and Dual Screening: Analyzing Roles, Content and Motivations in Political Twitter Conversations
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In: Politics and Governance ; 8 ; 2 ; 311-325 ; Policy Debates and Discourse Network Analysis (2021)
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| 12 |
“La maldición de Babel”. Crónicas periodísticas del nacionalismo lingüístico español ; “The curse of Babel”. Journalism chronicles of Spanish linguistic nationalism
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| 14 |
On Multi-domain Sentence Level Sentiment Analysis for Roman Urdu
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Abstract:
Sentiment analysis, or opinion mining, is a computational process to determine the polarity of a topic, opinion, emotion, or attitude. Most of the work done onsentiment analysis is for resource-rich languages, such as English and Chinese. However, only limited work has been done for Roman Urdu/Hindi, which is hence a resource-poor language. Developing a robust Sentiment analysis system for Roman Urdu/Hindi is necessitated due to two major reasons. First, Urdu/Hindi is the third largest spoken language in the world, with over 500 million speakers. Second, it is becoming increasingly used because people prefer to communicate on the web using Latin Script (26 English Alphabets), instead of typing using their language-specific keyboards.Since the work on Roman Urdu/Hindi sentiment analysis is still in its infancy stage, therefore an urgent development of new techniques and improvements inexisting techniques is required. In particular, the development of an automated technique to address the problem of Roman Urdu/Hindi text normalization is necessary as that widely affects the performance of all Natural Language Processing applications, including Sentiment classification. The non-availability of an annotated dataset is another major issue towards building effective techniques for Roman Urdu/Hindi sentiment analysis.In this thesis, challenging issues hindering the development of effective Roman Urdu/Hindi sentiment classification have been addressed. First, the largest-everdataset of 11000 Roman Urdu/Hindi reviews has been gathered from six different domains, using comprehensive annotation guidelines. Second, a machine learning-based Roman Urdu sentiment analysis is developed using different content-based features. Third, a novel feature selection technique, called Discriminative Feature Spamming Technique, has been developed for Roman Urdu/Hindi sentiment analysis. This technique identifies distinctive features based on a term utility criteria and then further increases their discriminative power by spamming them. The spelling variation problem inherent to Roman Urdu/Hindi adversely affects the performance of the machine learning algorithms. Therefore, in the next step, an open and hard problem of Roman Urdu/Hindi word normalization has been addressed by developing an automated lexical normalizer. The encoder maps differing spellings of a single Roman Urdu/Hindi word to a single common code, via a transliteration-based technique. This technique will have broad implications over different natural language processing applications. In addition, it will provide a concrete foundation to the research community to develop tools to automatically transliterate Urdu to Roman and vice-versa.
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Keyword:
Artificial Intelligence; Machine Learning; Natural Language Processing; Natural languages; Opinion mining; Pattern recognition; Resource poor language; Roman Urdu; Roman Urdu sentiment analysis; Urdu
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URL: http://handle.unsw.edu.au/1959.4/70709
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:74652/SOURCE02?view=true
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| 15 |
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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| 17 |
Using Twitter Streams for Opinion Mining: a case study on Airport Noise
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In: ISSN: 1865-0929 ; Communications in Computer and Information Science ; https://hal.archives-ouvertes.fr/hal-03018998 ; Communications in Computer and Information Science, Springer Verlag, 2020, ⟨10.1007/978-3-030-44900-1_10⟩ (2020)
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| 18 |
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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| 19 |
Improving Sentiment Polarity Detection through Target Identification
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Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts
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