A Thorough Experimental Evaluation of Algorithms for Opinion Mining in Albanian

Kote, Nelda; Biba, Marenglen; Trandafili, Evis

doi:10.1007/978-3-319-75928-9_47

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The authors conclude that, in general, to achieve higher accuracy in sentiment analysis, a larger corpus in the Albanian language is needed. Kote et al [14] presented a comprehensive experimental evaluation of machine learning algorithms applied for opinion mining in the Albanian language. Among the algorithms tested that performed better include logistic and multi-class classifier, hyper pipes, radial basis function (RBF) classifier, and RBF network with the classification accuracy ranging from 79% to 94%.…”

Section: Sentiment Analysis and Emotion Detectionmentioning

confidence: 99%

Natural language processing for Albanian: a state-of-the-art survey

Kastrati¹,

Biba²

2022

IJECE

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<span lang="EN-US">Due to its wide applicability, natural language processing (NLP) has attracted significant research efforts to the machine learning and deep learning research community. Despite this, research works investigating NLP for the Albanian language are still limited. However, to the best of our knowledge, there is no literature review available, which presents a clear picture of what has been studied, argued, and established in the area. The main objective of this survey is to comprehensively review, analyze and discuss the state-of-the-art in NLP for the Albanian language. Here, we present an extensive study concerning the contribution of several authors that have contributed to the application of NLP to the Albanian language. Also, we present an overview of research carried out in the typical applications of NLP for the Albanian language. Finally, some future challenges and limitations of the area are discussed.</span>

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Section: Sentiment Analysis and Emotion Detectionmentioning

confidence: 99%

Natural language processing for Albanian: a state-of-the-art survey

Kastrati¹,

Biba²

2022

IJECE

Self Cite

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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%

“…In [27], a comprehensive selection of ML algorithms is evaluated for opinion mining in the Albanian language, resulting to five best performing algorithms, Logistic and Multi Class Classifier, Hyper Pipes, RBF Classifier, and RBF Network with 79% to 94% of correctly classified instances. The opinions are classified as positive or negative.…”

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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“…In the Albanian language, pronouns are classified into subtypes according to their specificity. Furthermore, the relative pronoun is different from an interrogative pronoun or a personal pronoun [10]. Each distinguished type of pronoun has its own tag, as shown in Table 4 below.…”

Section: Part-of-speech (Pos) Taggingmentioning

confidence: 99%

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

Mati¹,

Hamiti²,

Susuri³

et al. 2021

AETiC

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The development of natural language processing resources for Albanian has grown steadily in recent years. This paper presents research conducted on unsupervised learning-the challenges associated with building a dictionary for the Albanian language and creating part-of-speech tagging models. The majority of languages have their own dictionary, but languages with low resources suffer from a lack of resources. It facilitates the sharing of information and services for users and whole communities through natural language processing. The experimentation corpora for the Albanian language includes 250K sentences from different disciplines, with a proposal for a part-of-speech tagging tag set that can adequately represent the underlying linguistic phenomena. Contributing to the development of Albanian is the purpose of this paper. The results of experiments with the Albanian language corpus revealed that its use of articles and pronouns resembles that of more high-resource languages. According to this study, the total expected frequency as a means for correctly tagging words has been proven effective for populating the Albanian language dictionary.

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A Thorough Experimental Evaluation of Algorithms for Opinion Mining in Albanian

Cited by 5 publications

References 16 publications

Natural language processing for Albanian: a state-of-the-art survey

Natural language processing for Albanian: a state-of-the-art survey

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

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

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