An Experimental Evaluation of Algorithms for Opinion Mining in Multi-domain Corpus in Albanian

Kote, Nelda; Biba, Marenglen; Trandafili, Evis

doi:10.1007/978-3-030-01851-1_42

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…On the other hand, sentiment analysis for the Albanian language stands behind even some other low resource languages, with only few works on sentiment analysis (opinion mining) [11 , 12] , emotion detection [13] and hate speech [14] . As deficiency of an Albanian language larger corpus of data is what these works characterize, being a prerequisite to develop a high-performance sentiment classifier for opinion mining, the dataset presented in this article aims to exactly address that low resource drawback typical for low-resource languages.…”

Section: Data Descriptionmentioning

confidence: 99%

Human-annotated dataset for social media sentiment analysis for Albanian language

et al. 2022

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Section: Data Descriptionmentioning

confidence: 99%

Human-annotated dataset for social media sentiment analysis for Albanian language

et al. 2022

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“…There are only a few works on sentiment analysis (opinion mining) in the Albanian language [26][27][28], as well as few related to sentiment analysis on emotion detection in the Albanian language [29,30].…”

Section: Related Workmentioning

confidence: 99%

“…The results varied also from subject to subject. This research is later extended from an in-domain corpus to multi-domains corpuses combining opinions from 5 different topics [28]. All the corpuses are used to train and test for opinion mining the performance of 50 classification algorithms implemented in Weka.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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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%.

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Question Classification for Albanian Language: An Annotated Corpus and Classification Models

Kote

Trandafili²,

Plepi³

2022

Lecture Notes in Networks and Systems

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An Experimental Evaluation of Algorithms for Opinion Mining in Multi-domain Corpus in Albanian

Cited by 3 publications

References 16 publications

Human-annotated dataset for social media sentiment analysis for Albanian language

Human-annotated dataset for social media sentiment analysis for Albanian language

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

Question Classification for Albanian Language: An Annotated Corpus and Classification Models

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