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Assessing the use of back translation: the shortcomings of back translation as a quality testing method
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In: International Journal of Social Research Methodology ; 20 ; 6 ; 573-584 (2021)
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| 22 |
Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets
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In: Politics and Governance ; 8 ; 2 ; 326-339 ; Policy Debates and Discourse Network Analysis (2021)
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| 23 |
Measurement Invariance Across Immigrant and Non-Immigrant Populations on PISA Cognitive and Non-Cognitive Scales
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In: Doctoral Dissertations (2021)
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| 24 |
Comparing textual genres in Spanish: the case of the tourism domain
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In: Ibérica, Iss 42, Pp 163-190 (2021) (2021)
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| 25 |
Approches quantitatives de l'analyse des prédictions en traduction automatique neuronale (TAN)
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In: JADT 2020 : 15èmes Journées Internationales d'Analyse statistique des Données Textuelles ; https://hal.archives-ouvertes.fr/hal-03049589 ; JADT 2020 : 15èmes Journées Internationales d'Analyse statistique des Données Textuelles, Université de Toulouse, Jun 2020, Toulouse, France ; https://jadt2020.sciencesconf.org (2020)
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| 26 |
La textométrie en question
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In: ISSN: 0015-9409 ; Le Français Moderne - Revue de linguistique Française ; https://halshs.archives-ouvertes.fr/halshs-02902088 ; Le Français Moderne - Revue de linguistique Française, CILF (conseil international de la langue française), 2020, Linguistique et traitements quantitatifs, 88 (1), pp.26-43 ; http://www.le-francais-moderne.com/ (2020)
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| 27 |
Применение фильтра Калмана для навигационных задач железнодорожного транспорта ... : выпускная квалификационная работа бакалавра ...
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Хатымов, Ренат. - : Санкт-Петербургский политехнический университет Петра Великого, 2020
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| 28 |
Doing Linguistics with a Corpus: Methodological Considerations for the Everyday User. Jesse Egbert, Tove Larsson, Douglas Biber ...
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| 29 |
Economic and mathematical modeling of risks of introduction of information and communication technologies ...
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| 30 |
Economic and mathematical modeling of risks of introduction of information and communication technologies ...
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| 31 |
Statistical Complexity Analysis of Turing Machine tapes with Fixed Algorithmic Complexity Using the Best-Order Markov Model
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In: Entropy ; Volume 22 ; Issue 1 (2020)
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| 32 |
Analyzing Given Names ... : Exploring Potentials for a Personalized Name Discovery on Nameling.net ...
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| 33 |
THE PROBLEM OF GENERAL UNDERDEVELOPMENT OF SPEECH IN CHILDREN OF PRESCHOOL AGE ...
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| 34 |
Opinion-Mining on Marglish and Devanagari Comments of YouTube Cookery Channels Using Parametric and Non-Parametric Learning Models
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In: Big Data and Cognitive Computing ; Volume 4 ; Issue 1 (2020)
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Abstract:
YouTube is a boon, and through it people can educate, entertain, and express themselves about various topics. YouTube India currently has millions of active users. As there are millions of active users it can be understood that the data present on the YouTube will be large. With India being a very diverse country, many people are multilingual. People express their opinions in a code-mix form. Code-mix form is the mixing of two or more languages. It has become a necessity to perform Sentiment Analysis on the code-mix languages as there is not much research on Indian code-mix language data. In this paper, Sentiment Analysis (SA) is carried out on the Marglish (Marathi + English) as well as Devanagari Marathi comments which are extracted from the YouTube API from top Marathi channels. Several machine-learning models are applied on the dataset along with 3 different vectorizing techniques. Multilayer Perceptron (MLP) with Count vectorizer provides the best accuracy of 62.68% on the Marglish dataset and Bernoulli Naï ; ve Bayes along with the Count vectorizer, which gives accuracy of 60.60% on the Devanagari dataset. Multilayer Perceptron and Bernoulli Naï ; ve Bayes are considered to be the best performing algorithms. 10-fold cross-validation and statistical testing was also carried out on the dataset to confirm the results.
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Keyword:
bernoulli naïve bayes; count vectorizer; cross-validation; devanagari; logistic regression; marglish; sentiment analysis; statistical testing; TF-IDF vectorizer; YouTube
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URL: https://doi.org/10.3390/bdcc4010003
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| 35 |
Development of Gaussian Learning Algorithms for Early Detection of Alzheimer's Disease
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In: FIU Electronic Theses and Dissertations (2020)
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| 36 |
The Quality of Big Data: Development, Problems, and Possibilities of Use of Process-Generated Data in the Digital Age
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In: Historical Social Research ; 45 ; 3 ; 209-243 (2020)
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| 37 |
Adults' Identity in Acculturation Settings: The Multigroup Ethnic & National Identity Measure (MENI)
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In: Identity: An International Journal of Theory and Research ; 19 ; 4 ; 245-257 (2020)
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| 38 |
A rationale for unanimity in committees
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In: SP II 2017-308 ; Discussion Papers / Wissenschaftszentrum Berlin für Sozialforschung, Forschungsschwerpunkt Markt und Entscheidung, Abteilung Ökonomik des Wandels ; 41 (2020)
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| 39 |
An English-language adaptation of the Interpersonal Trust Short Scale (KUSIV3)
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In: Measurement Instruments for the Social Sciences ; 1-12 (2020)
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Adjusting for treatment switching in oncology trials: a systematic review and rRecommendations for reporting
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