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Big Data Cogn. Comput., Volume 4, Issue 1 (March 2020) – 3 articles

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Cover Story (view full-size image) In recent years, we have witnessed an increase in the quantities of available digital textual data, [...] Read more.
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Open AccessArticle
Opinion-Mining on Marglish and Devanagari Comments of YouTube Cookery Channels Using Parametric and Non-Parametric Learning Models
Big Data Cogn. Comput. 2020, 4(1), 3; https://doi.org/10.3390/bdcc4010003 - 17 Mar 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 [...] Read more.
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 Naïve Bayes along with the Count vectorizer, which gives accuracy of 60.60% on the Devanagari dataset. Multilayer Perceptron and Bernoulli Naïve 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. Full article
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Open AccessEditorial
Acknowledgement to Reviewers of Big Data and Cognitive Computing in 2019
Big Data Cogn. Comput. 2020, 4(1), 2; https://doi.org/10.3390/bdcc4010002 - 06 Feb 2020
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Rigorous peer-review is the corner-stone of high-quality academic publishing [...] Full article
Open AccessArticle
Text Mining in Big Data Analytics
Big Data Cogn. Comput. 2020, 4(1), 1; https://doi.org/10.3390/bdcc4010001 - 16 Jan 2020
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Abstract
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine [...] Read more.
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined. Full article
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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