Idioma:

A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages

Kastrati, Zenun ; Ahmedi, Lule ; Kurti, Arianit ; Kadriu, Fatbardh ; Murtezaj, Doruntina ; Gashi, Fatbardh

Electronics (Basel), 2021-05, Vol.10 (10), p.1133 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por

Enviar para

Título:
A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages
Autor: Kastrati, Zenun ; Ahmedi, Lule ; Kurti, Arianit ; Kadriu, Fatbardh ; Murtezaj, Doruntina ; Gashi, Fatbardh
Assuntos: 1D-CNN ; attention mechanism ; BiLSTM ; Computer and Information Sciences Computer Science ; Coronaviruses ; COVID-19 ; Data mining ; Data- och informationsvetenskap ; Datasets ; Deep learning ; deep neural network ; Digital media ; Languages ; machine learning ; Pandemics ; Sentiment analysis ; Social networks
É parte de: Electronics (Basel), 2021-05, Vol.10 (10), p.1133
Descrição: During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.
Editor: Basel: MDPI AG
Idioma: Inglês

Links

View record from Swedish Publication Index

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages

Kastrati, Zenun ; Ahmedi, Lule ; Kurti, Arianit ; Kadriu, Fatbardh ; Murtezaj, Doruntina ; Gashi, Fatbardh

Basel: MDPI AG

Buscando em bases de dados remotas. Favor aguardar.