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Linguistic Resources Linguistic Resources for the automatic for the automatic annotation of speech annotation of speech (version 6)
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In: https://hal.archives-ouvertes.fr/hal-03468442 ; 2021 (2021)
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Slavonic–Romanian lexicons of the 17th century and their comparative digital edition (the eRomLex project)
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Sentiment analysis of extremism in social media from textual information
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In: Security Research Hub Reports (2020)
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Constraints on the lexicons of human languages have cognitive roots present in baboons ( Papio papio )
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In: ISSN: 0027-8424 ; EISSN: 1091-6490 ; Proceedings of the National Academy of Sciences of the United States of America ; https://hal.archives-ouvertes.fr/hal-02935155 ; Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2019, 116 (30), pp.14926-14930. ⟨10.1073/pnas.1907023116⟩ (2019)
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Incorporating Semantic Information to FastText Word Vectors for Improved Sentiment Analysis
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In: Dissertations (2019)
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LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification
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In: Applied Sciences ; Volume 9 ; Issue 24 (2019)
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Abstract:
Sentiment Analysis (SA) is an active research area. SA aims to classify the online unstructured user-generated contents (UUGC) into positive and negative classes. A reliable training data is vital to learn a sentiment classifier for textual sentiment classification, but due to domain heterogeneity, manually construction of reliable labeled sentiment corpora is a laborious and time-consuming task. In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the research community. However, state-of-the-art techniques for semi-supervised sentiment classification present research challenges expressed in questions like the following. How to effectively utilize the concealed significant information in the unstructured data? How to learn the model while considering the most effective sentiment features? How to remove the noise and redundant features? How to refine the initial training data for initial model learning as the random selection may lead to performance degradation? Besides, mainly existing lexicons have trouble with word coverage, which may ignore key domain-specific sentiment words. Further research is required to improve the sentiment lexicons for textual sentiment classification. In order to address such research issues, in this paper, we propose a novel unified sentiment analysis framework for textual sentiment classification called LeSSA. Our main contributions are threefold. (a) lexicon construction, generating quality and wide coverage sentiment lexicon. (b) training classification models based on a high-quality training dataset generated by using k-mean clustering, active learning, self-learning, and co-training algorithms. (c) classification fusion, whereby the predictions from numerous learners are confluences to determine final sentiment polarity based on majority voting, and (d) practicality, that is, we validate our claim while applying our model on benchmark datasets. The empirical evaluation of multiple domain benchmark datasets demonstrates that the proposed framework outperforms existing semi-supervised learning techniques in terms of classification accuracy.
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Keyword:
active learning; classification fusion; co-training; initial training set; self-training; semi-supervised learning; sentiment lexicons; textual sentiment classification
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URL: https://doi.org/10.3390/app9245562
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CUSTOMER OPINIONS EVALUATION: A CASESTUDY ON ARABIC TWEETS ...
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CUSTOMER OPINIONS EVALUATION: A CASESTUDY ON ARABIC TWEETS ...
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Cross-domain and Cross-lingual abusive language detection: A hybrid approach with deep learning and a multilingual lexicon
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Patti V.; Pamungkas E.. - : Association for Computational Linguistics (ACL), 2019. : country:USA, 2019. : place:Stroudsburg, 2019
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A multilingual collection of CoNLL-U-compatible morphological lexicons
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In: Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ; https://hal.inria.fr/hal-01798798 ; Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 2018, Miyazaki, Japan ; http://lrec2018.lrec-conf.org/en/ (2018)
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Commercial and economic relations of Russia and England / Great Britain as the motivational drivers to the Russian language learning
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Building Phrase Polarity Lexicons for Sentiment Analysis
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Dehkharghani, Rahim. - : International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 2018
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Constructing lexicons of relational phrases ... : Konstruktion von Lexika von relationalen Phrasen ...
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Kayaw-English-Myanmar Dictionary ; Kayaw - Kalǎ - Ghò̄tò Cèphlô̄bè̄htǔ ; ကယော - အင်္ဂလိပ် - မြန်မာ အဘိဓါန်
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Spanish Morphological Generation with Wide-Coverage Lexicons and Decision Trees ; Generación Morfológica del Español con Lexicones de Amplia Cobertura y Árboles de Decisión
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Constructing lexicons of relational phrases ; Konstruktion von Lexika von relationalen Phrasen
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Induction de lexiques bilingues à partir de corpus comparables et parallèles
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El léxico español del vuelo: un proyecto lexicográfico para el dominio del transporte aéreo
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In: LFE. Revista de lenguas para fines específicos [eISSN 2340-8561], v. 23 (2), p. 290-312 (2017)
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НОВЫЕ ГОРИЗОНТЫ В ПИСАТЕЛЬСКОЙ ЛЕКСИКОГРАФИИ И В ИЗУЧЕНИИ ИСТОРИЧЕСКОЙ ЛИНГВОПОЭТИКИ РУССКОЙ ЛИТЕРАТУРЫ
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ВАСИЛЬЕВ Н.Л.. - : Общество с ограниченной ответственностью "Издательский Дом "Академия Естествознания", 2016
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МОДЕЛИ РЕЧЕВОГО ПОРТРЕТА И ЛИНГВОПСИХИЧЕСКИЙ ПОРТРЕТ ДРАМАТИЧЕСКИХ ПЕРСОНАЖЕЙ В ПЬЕСЕ А.П. ЧЕХОВА «ДЯДЯ ВАНЯ»
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ЦЗЯН ЧЖИЯНЬ. - : Негосударственное образовательное частное учреждение дополнительного профессионального образования «Кубанская многопрофильная академия подготовки, переподготовки, повышения квалификации специалистов», 2016
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