User-Emotion Detection Through Sentence-Based Classification Using Deep Learning: A Case-Study with Microblogs in Albanian

Skënduli, Marjana Prifti; Biba, Marenglen; Loglisci, Corrado; Ceci, Michelangelo; Malerba, Donato

doi:10.1007/978-3-030-01851-1_25

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Over the last few years, sentiment analysis is one of the most active research areas in NLP [35]. However, there are only a few researches dedicated to sentiment analysis (opinion mining) for the Albanian language presented by authors in [13]- [16] and a few others related to emotion detection in studies [17], [18].…”

Section: Sentiment Analysis and Emotion Detectionmentioning

confidence: 99%

“…Studies conducted concerning tools include Int J Elec & Comp Eng ISSN: 2088-8708  Natural language processing for Albanian: A state-of-the-art survey (Muhamet Kastrati) 6433 part-of-speech and morphological tagging [3]- [5], stemming [6], the lexicon of Albanian for NLP [7], and syntactic parsing [8]. Also, there are several studies related to the application of the NLP for the Albanian language such as named-entity recognition [9]- [12], sentiment analysis [13]- [16], emotion detection [17], [18], hate speech detection [19], [20], summarization techniques [21], [22], text classification [23]- [25] and question answering system [26].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

Kastrati¹,

Biba²

2022

IJECE

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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 [29], a CNN sentence-based classifier is developed to classify a given text fragment into one out of six pre-defined emotion classes based on Ekman' model: joy, fear, disgust, anger, shame and sadness. Experimental evaluation shows that a deep learning model (CNN) with classification accuracy of emotions ranging from 67% to 92.4% in overall outperforms three classical classification algorithms, Naive Bayes (NB), Instance-based learner (IBK), and Support Vector Machines (SMO).…”

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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“…Emosi menambah cita rasa hidup dengan memperkenalkan cara mengekspresikan perasaan mereka dalam bentuk komunikasi [1]. Analisis emosi manusia telah menjadi topik penelitian dalam berbagai disiplin ilmu, seperti Ilmu Kognitif, Psikologi, dan berkat difusi media sosial, itu juga menarik minat para ilmuwan komputer [2]. Memahami emosi memainkan peran utama  71…”

Section: Pendahuluanunclassified

Deteksi Kondisi Emosi pada Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing

Rohman¹,

Utami²,

Raharjo³

2019

Jurnal Eksplora Informatika

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Emosi memenuhi kehidupan manusia setiap waktu. Emosi mempengaruhi hubungan sosial, ingatan dan bahkan dalam pengambilan keputusan. Saat ini, orang cenderung mengekspresikan emosi melalui media sosial seperti Facebook dalam bentuk gambar, video dan teks pada umumnya. Deteksi emosi pada teks merupakan bidang penelitian yang baru dan banyak diteliti khususnya dibidang linguistik. Penelitian ini menggunakan EmoLex sebagai leksikon yang digunakan untuk mendeteksi emosi pada suatu teks. Kosa kata pada EmoLex diperluas dengan pencarian sinonim menggunakan Kateglo API. EmoLex digunakan sebagai leksikon 8 kategori emosi Plutchik dan sentimen. EmoLex tersedia dalam 105 bahasa berbeda termasuk Indonesia yang mana mengandung 14.182 kata yang kemudian diperluas dengan pencarian sinonim menggunakan Kateglo API. Pencarian sinonim menghasilkan 20.690 kata sehingga memperoleh hasil akhir leksikon emosi yang berisi 34.872 kata. Pengujian menunjukkan bahwa leksikon emosi mampu mendeteksi 55.45% atau 15.357 dari 27.696 kata yang diperoleh dari update status pengguna Facebook dalam melakukan pendeteksian emosi, sebanyak 100 update status diambil dari Facebook. Selanjutnya update status tersebut diperbaiki menggunakan Natural Language Processing (NLP). Hasil perbaikannya dinilai dengan leksikon emosi yang telah dibuat sebelumnya. 26 dari 100 update status dapat diketahui label emosinya. Hasil validasi terdapat 16 update status atau 61,53% label emosinya akurat.

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User-Emotion Detection Through Sentence-Based Classification Using Deep Learning: A Case-Study with Microblogs in Albanian

Cited by 6 publications

References 19 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

Deteksi Kondisi Emosi pada Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing

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