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4 results for “Sentiment Polarity”
Sentiment polarity lexicon of Bosnian language
<p>First sentiment annotated lexicon of the Bosnian language.</p> <p>The lexicon is divided into two files: positive and negative polarity.</p> <p>The lists are prepared in separate files, each file populated by words of the aforementioned polarity listed each word in a separate row. </p> <p>The positive polarity list BOSNIAN_POSITIVE.txt holds 1219 words.</p> <p>The negative polarity list BOSNIAN_NEGATIVE.txt holds 3935 words.</p>
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers
<p>These datasets are part of the submitted paper for the LREC2022 conference entitled: "The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers"</p> <p>The various data sources, as well as the methodology, are explained in detail in the research paper which will be available soon.</p> <p>ALPIN stands for Austrian Language Polarity in Newspapers. The dictionary consists of three different parts which were merged together:</p> <ul> <li>Austrian Media Corpus: AMC (AMC_v1.0.csv)</li> <li>STANDARD posts: STP (STP_v1.0.csv)</li> <li>Austriacisms: AUT (AUT_v1.0.csv)</li> </ul> <p>Austrian Media Corpus (AMC) (Ransmayr et al., 2017) & STANDARD posts (STP) (Schabus et al., 2017) rely on the SPLM algorithm as used in SentiDraw (Sharma & Dutta 2021). Austriacisms (AUT) was generated by using the Best-Worst scaling (BWS) (Kiritchenko and Mohammad, 2017b). The AUT list was collected from the “Variantenwörterbuch des Deutschen” (Ammon et al., 2016) (thereby only selecting those words that only surface in Austrian German and in no other variety of German) and an austriacism list of Wikipedia (https://de.wikipedia.org/wiki/Liste_von_Austriazismen).</p> <p>The scores are scaled to the interval [-1, 1] using the min-max-abs scaling, ranging from negative to positive.</p> <p>References:<br> Sharma, S. S., & Dutta, G. (2021). SentiDraw: Using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination. Information Processing & Management, 58(1), 102412.<br> Kiritchenko, S. and Mohammad, S. M. (2017b). Capturing reliable fine-grained sentiment associations by crowdsourcing and best-worst scaling.<br> Schabus, D., Skowron, M., & Trapp, M. (2017). One Million Posts: A Data Set of German Online Discussions. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1241–1244. https://doi.org/10.1145/3077136.3080711<br> Ransmayr, J., Mörth, K., & Ďurčo, M. (2017). AMC (Austrian Media Corpus). In Korpusbasierte Forschungen zum österreichischen Deutsch. In Digitale Methoden der Korpusforschung in Österreich (= Veröffentlichungen zur Linguistik und Kommunikationsforschung Nr. 30) (pp. 27–38). Verlag der Österreichischen Akademie der Wissenschaften.<br> Ammon, U., Bickel, H., & Ebner, J. (2016). Variantenwörterbuch des Deutschen : die Standardsprache in Österreich, der Schweiz, Deutschland, Liechtenstein, Luxemburg, Ostbelgien und Südtirol sowie Rumänien, Namibia und Mennonitensiedlungen. Walter de Gruyter.</p>
Domain-Independent Reviews' Sentiment Polarity Classification using Shallow Word2Seq Convolutional Neural Network
<p>Reviews and comments are perceptions about specific services or products. They are embedded with hidden sentiments the reviewer has towards certain subjects. Business owners use customer reviews to understand customers’ perceptions about specific services or products. The ability to understand reviews’ sentiment from different domains or areas give decision makers and business owners the opportunity to make critical business decisions which can help them to increase profits of their businesses. Previous studies had focussed on classifying sentiment polarity by using traditional machine learning and deep learning methods. However, these suffered from low model generalization ability, causing the models to perform better only on single domain datasets rather than multiple domain datasets. The problem is the inability of the classification model to learn domain-restricted knowledge from multi-domain datasets. Aiming to improve the accuracy of the cross-domain classification, this paper proposes a method which uses Word2Seq Convolutional Neural Network (CNN) to classify reviews’ sentiment across multiple domain datasets (i.e. digital worker, movie, product, hotel and restaurant reviews). The evaluation showed that the proposed method had achieved the state-of-the-art performance. The high classification performance also promoted the reliability and effectiveness of implementing the Word2Seq CNN to classify reviews’ sentiment across different domains and learn domain restricted knowledge while improving the model generalization ability.</p> <p>The uploaded dataset is a sampled dataset with 5000 observations for both training and testing sets.</p>
Sentiment Polarity of Programmers in an Open Source Software Project: An Exploratory Study
<p>Context: During the implementation of issues filed in open source software projects, programmers engage and interact in discussions on how to implement them. These discussions provide evidence to investigate emotionally loaded practices embraced by programmers. They interact to explain their point of view regarding the project and the issue under analysis. Objective: Analyze programmers sentiment polarity in an open source software project having releases as a reference for the analysis. Methods: We conducted an exploratory study to characterize the sentiment polarity of comments registered in issues associated with releases of the Moby open source software project. Results: The quantitative analysis identified sentiment polarity variations throughout consecutive releases in line with specific functionalities. Based on a qualitative analysis, we identified these functionalities and specific group of programmers that contributed to those results. Conclusions: We identified initial evidence to contribute for the understanding of the causes underlying the influence of the sentiments of the developers in the context of releases of open source software projects.</p>
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