NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis

Muhammad, Shamsuddeen Hassan; Adelani, David Ifeoluwa; Ruder, Sebastian; Ahmad, Ibrahim Said; Abdulmumin, Idris; Bello, Bello Shehu; Choudhury, Monojit; Emezue, Chris Chinenye; Abdullahi, Saheed Salahudeen; Aremu, Anuoluwapo; Jeorge, Alipio; Brazdil, Pavel

doi:10.48550/arxiv.2201.08277

Cited by 6 publications

(12 citation statements)

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“…However, few methods focus on self-learning sentiment analysis classification, with less attention paid to multilingual contexts. Recently, a study by [106] used Twitter to develop NaijaSenti corpus (i.e. languages such as Hausa, Igbo, Pidgin, and Yorùbá), and they evaluated their corpus using mBERT, XLM-R and Roberta.…”

Section: B Discussionmentioning

confidence: 99%

“…Despite the advances in NLP, many under-resourced languages are still not covered by pre-trained models like BERT, RoBERTa, and XML-RoBERTa. The use of finetuning language models is one strategy for addressing this problem [106].…”

Section: Emerging Msa Areasmentioning

confidence: 99%

“…sentiment analysis for monolingual, code-switched and multilingual comments in under-resourced languages has been studied only for a few African languages, e.g. several Nigerian languages [99], [106], Swahili [114] and Bambara [18]. Annotated datasets for MSA are lacking.…”

Section: Emerging Msa Areasmentioning

confidence: 99%

“…A study by [106] investigated the development of Nai-jaSenti -an introduction of the first African large-scale human-annotated dataset for sentiment analysis in Nigerian languages (i.e. Igbo, Yoruba, Hausa and Nigerian-Pidgin).…”

Section: Emerging Msa Areasmentioning

confidence: 99%

“…AfriBERTa has been trained in 11 languages: Afaan Oromoo (also known as Oromo), Amharic, Gahuza (i.e. a hybrid language that includes Kinyarwanda and Kirundi), Hausa, Igbo, Nigerian Pidgin, Somali, Swahili, Tigrinya, and Yoruba [106], [116]. AfriBERTa was tested for named entity recognition and text categorisation in ten languages.…”

Section: Emerging Msa Areasmentioning

confidence: 99%

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Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

2023

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Sentiment analysis automatically evaluates people's opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis (MSA) techniques for under-resourced languages and the use of high-resourced languages to develop resources for MSA in low-resource languages, with the ultimate goal of identifying appropriate strategies for future MSA investigations. We report over 35 studies with different languages demonstrating an interest in developing MSA models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for the MSA task. Our focus is critically comparing MSA methods and employed datasets and identifying research gaps. Our comparative analysis study contributes to theoretical literature reviews with complete coverage of MSA studies from 2008 to date. Furthermore, we demonstrate how MSA studies have grown tremendously. Finally, because most studies propose MSA methods based on deep learning approaches, we offer a deep learning framework for MSA that does not rely on machine translation systems. According to the metaanalysis (PRISMA) protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the MSA performance. Therefore, deep learning methods are recommended for the development of MSA for under-resourced languages.

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Section: B Discussionmentioning

confidence: 99%

Section: Emerging Msa Areasmentioning

confidence: 99%

Section: Emerging Msa Areasmentioning

confidence: 99%

Section: Emerging Msa Areasmentioning

confidence: 99%

Section: Emerging Msa Areasmentioning

confidence: 99%

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Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

2023

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Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

Nakatumba‐Nabende,

Babirye,

Nabende

et al. 2024

Applied AI Letters

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Africa has over 2000 languages; however, those languages are not well represented in the existing natural language processing ecosystem. African languages lack essential digital resources to effectively engage in advancing language technologies. There is a need to generate high‐quality natural language processing resources for low‐resourced African languages. Obtaining high‐quality speech and text data is expensive and tedious because it can involve manual sourcing and verification of data sources. This paper discusses the process taken to curate and annotate text and speech datasets for five East African languages: Luganda, Runyankore‐Rukiga, Acholi, Lumasaba, and Swahili. We also present results obtained from baseline models for machine translation, topic modeling and classification, sentiment classification, and automatic speech recognition tasks. Finally, we discuss the experiences, challenges, and lessons learned in creating the text and speech datasets.

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Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Maruf¹,

Ziyad²,

Haque³

et al. 2022

IJACSA

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In the human body, emotion plays a critical function. Emotion is the most significant subject in human-machine interaction. In economic contexts, emotion detection is equally essential. Emotion detection is crucial in making any decision. Several approaches were explored to determine emotion in text. People increasingly use social media to share their views, and researchers strive to decipher emotions from this medium. There has been some work on emotion detection from the text and sentiment analysis. Although some work has been done in which emotion has been recognized, there are many things to improve. There is not much work to detect racism and analysis sentiment on Ukraine -Russia war. We suggested a unique technique in which emotion is identified, and the sentiment is analyzed. We utilized Twitter data to analyze the sentiment of the Ukraine-Russia war. Our system performs better than prior work. The study increases the accuracy of detecting emotion. To identify emotion and racism, we used classical machine learning and the ensemble method. An unsupervised approach and NLP modules were used to analyze sentiment. The goal of the study is to detect emotion and racism and also analyze the sentiment.

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NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis

Cited by 6 publications

References 0 publications

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

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