Do you want to publish a course? Click here

Analyzing University Students' Attitudes Through Social Media

تحليل توجهات الطلاب الجامعيين عن طريق وسائل التواصل الاجتماعي (تطبيق رأي خاص بطلاب الجامعة الافتراضية)

1220   2   1   0.0 ( 0 )
 Publication date 2020
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

No English abstract

References used
Bekkali, M., & Lachkar, A. (2019, March). Arabic Sentiment Analysis based on Topic Modeling. In Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society(p. 17). ACM.
Rao, K. P., Koneru, A., & Raju, D. N. (2019). OEFC Algorithm—Sentiment Analysis on Goods and Service Tax System in India. In Cognitive Informatics and Soft Computing (pp. 441-451). Springer, Singapore
Guellil, I., Adeel, A., Azouaou, F., & Hussain, A. (2018, July). Sentialg: Automated corpus annotation for algerian sentiment analysis. In International Conference on Brain Inspired Cognitive Systems (pp. 557-567). Springer, Cham
Abdellaoui, H., & Zrigui, M. (2018). Using tweets and emojis to build TEAD: an Arabic dataset for sentiment analysis. Computación y Sistemas, 22(3).
rate research

Read More

Abusive language is a growing phenomenon on social media platforms. Its effects can reach beyond the online context, contributing to mental or emotional stress on users. Automatic tools for detecting abuse can alleviate the issue. In practice, develo ping automated methods to detect abusive language relies on good quality data. However, there is currently a lack of standards for creating datasets in the field. These standards include definitions of what is considered abusive language, annotation guidelines and reporting on the process. This paper introduces an annotation framework inspired by legal concepts to define abusive language in the context of online harassment. The framework uses a 7-point Likert scale for labelling instead of class labels. We also present ALYT -- a dataset of Abusive Language on YouTube. ALYT includes YouTube comments in English extracted from videos on different controversial topics and labelled by Law students. The comments were sampled from the actual collected data, without artificial methods for increasing the abusive content. The paper describes the annotation process thoroughly, including all its guidelines and training steps.
This paper describes the Helsinki--Ljubljana contribution to the VarDial 2021 shared task on social media variety geolocation. Following our successful participation at VarDial 2020, we again propose constrained and unconstrained systems based on the BERT architecture. In this paper, we report experiments with different tokenization settings and different pre-trained models, and we contrast our parameter-free regression approach with various classification schemes proposed by other participants at VarDial 2020. Both the code and the best-performing pre-trained models are made freely available.
Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic mod els on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their correlations as edges. Further details are displayed interactively to support the exploration of large text collections, e.g., representative words and sentences of topics, topic and sentiment distributions, hierarchical topic clustering, and customizable, predefined topic labels. The toolkit optimizes automatically on custom data for optimal coherence. We show a working instance of the toolkit on data crawled from English social media discussions about organic food consumption. The visualization confirms findings of a qualitative consumer research study. SocialVisTUM and its training procedures are accessible online.
Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. In this work, we explore senti ment and product identification on tobacco-related text from two social media platforms. We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products' sentiment. We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT. Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.
The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based o n the face-threat that the complainer is willing to undertake. This is particularly useful for understanding the intent of complainers and how humans develop suitable apology strategies. In this paper, we study the severity level of complaints for the first time in computational linguistics. To facilitate this, we enrich a publicly available data set of complaints with four severity categories and train different transformer-based networks combined with linguistic information achieving 55.7 macro F1. We also jointly model binary complaint classification and complaint severity in a multi-task setting achieving new state-of-the-art results on binary complaint detection reaching up to 88.2 macro F1. Finally, we present a qualitative analysis of the behavior of our models in predicting complaint severity levels.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا