SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

Tobaili, Taha; Fernández, Miriam; Alani, Harith; Sharafeddine, Sanaa; Hajj, Hazem; Glavaš, Goran

doi:10.26615/978-954-452-056-4_138

Cited by 9 publications

(5 citation statements)

References 21 publications

(13 reference statements)

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“…In (Guellil et al, 2018), the approach consists to automatically classify sentiments of Algerian Arabizi after transliterating to MSA. (Tobaili et al, 2019) has created a sentiment lexicon for Arabizi. In a very recent work, (Fourati et al, 2020) has released a 3,000-comments sentiment analysis dataset named TUNIZI 5 , which has been collected from social networks, preprocessed and manually annotated by Tunisian native speakers.…”

Section: Arabizi and Sentiment Analysismentioning

confidence: 99%

Arabizi Language Models for Sentiment Analysis

Baert¹,

Gahbiche²,

Gadek

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. It is informal and does not follow any conventional rules, raising many NLP challenges. In particular, Arabizi has recently emerged as the Arabic language in online social networks, becoming of great interest for opinion mining and sentiment analysis. Unfortunately, only few Arabizi resources exist and state-of-the-art language models such as BERT do not consider Arabizi.In this work, we construct and release two datasets: (i) LAD, a corpus of 7.7M tweets written in Arabizi and (ii) SALAD, a subset of LAD, manually annotated for sentiment analysis. Then, a BERT architecture is pre-trained on LAD, in order to create and distribute an Arabizi language model called BAERT. We show that a language model (BAERT) pre-trained on a large corpus (LAD) in the same language (Arabizi) as that of the fine-tuning dataset (SALAD), outperforms a state-of-the-art multi-lingual pretrained model (multilingual BERT) on a sentiment analysis task.

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Section: Arabizi and Sentiment Analysismentioning

confidence: 99%

Arabizi Language Models for Sentiment Analysis

Baert¹,

Gahbiche²,

Gadek

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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show abstract

“…However, texts on social media, often containing opinions on various subjects, pose a challenge in determining the sentiment polarity of multiple aspects from a single sentence (Tobaili et al, 2019;Sahoo & Gupta, 2021). Zhou et al (2019) noted that lots of errors in sentiment classification arise from not considering the aspect words in sentences.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Complementary Correlation-Based Multimodal Aspect-Level Sentiment Analysis

Yang,

Xiong

2024

International Journal on Semantic Web and Information Systems

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Aspect-based sentiment analysis is the key to natural language processing, and it focuses on the polarity of emotions associated with specific text aspects. Traditional models that combine text and visual data tend to ignore the deeper interconnections between patterns. To solve this problem, the authors propose a multimodal sentiment-oriented analysis (BiCCM-ABSA) model based on bidirectional complementary correlation. The model utilizes text-image synergy through a novel cross-modal attention mechanism to align text with image features. With the transformer architecture, it is not only a simple fusion, but also ensures the complex alignment of multi-modal features and gating mechanisms. Experiments were conducted on the Twitter-15 and Twitter-17 datasets, achieving 69.28 accuracy and 67.54% F1 score, respectively. The experimental results demonstrate the advantages of BiCCM-ABSA, the bidirectional approach of the model and the effective cross-modal correlation set a new benchmark in the field of multimodal emotion recognition, providing insights beyond traditional single-modal analysis.

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“…From the work of Guellil et al [3] published in 2021, the DA is divided into six collections: i) Maghrebi (MAGH), ii) Egyptian (EGY), iii) Iraqi (IRQ), iv) Levantine (LEV), v) Gulf (GLF), and vi) others remaining dialect. On the other hand, the Arabic language used on short messaging system (SMS), chat forums and on social media generally is called "Arabizi" [4]. Its written text is a mixture of Latin characters, numerals and some punctuation.…”

Section: Introductionmentioning

confidence: 99%

AraBERT transformer model for Arabic comments and reviews analysis

Moubtahij

Abdelali

Tazi

2022

IJ-AI

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Arabic language is rich and complex in terms of word morphology compared to other Latin languages. Recently, natural language processing (NLP) field emerges with many researches targeting Arabic language understanding (ALU). In this context, this work presents our developed approach based on the Arabic bidirectional encoder representations from transformers (AraBERT) model where the main required steps are presented in detail. We started by the input text pre-processing, which is, then, segmented using the Farasa segmentation technique. In the next step, the AraBERT model is implemented with the pertinent parameters. The performance of our approach has been evaluated using the ARev dataset which contains more than 40,000 comments-remarks records relate to the tourism sector such as hotel reviews, restaurant reviews and others. Moreover, the obtained results are deeply compared with other relevant states of the art methods, and it shows the competitiveness of our approach that gives important results that can serve as a guide for further improvements in this field.

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SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

Cited by 9 publications

References 21 publications

Arabizi Language Models for Sentiment Analysis

Arabizi Language Models for Sentiment Analysis

Bidirectional Complementary Correlation-Based Multimodal Aspect-Level Sentiment Analysis

AraBERT transformer model for Arabic comments and reviews analysis

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