An Enhanced Feature Acquisition for Sentiment Analysis of English and Hausa Tweets

Abubakar, Amina; Roko, Abubakar; Bui, Aminu Muhammad; Saidu, Ibrahim

doi:10.14569/ijacsa.2021.0120913

Cited by 7 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 4, Query expansion using WordNet: WordNet is an online lexical system (database) for the English language that provides diverse and wide-ranging semantic information [34]. We used query expansion to add related words to a query to increase the number of returned documents and improve the recall accordingly.…”

Section: Phase 2: Choosing and Applying Clustering Modelsmentioning

confidence: 99%

Clustering-based Automated Requirement Trace Retrieval

Al-walidi¹,

Azab²,

Khamis³

et al. 2022

IJACSA

View full text Add to dashboard Cite

The benefits of requirement traceability are well known and documented. The traceability links between requirements and code are fundamental in supporting different activities in the software development process, including change management and software maintenance. These links can be obtained using manual or automatic means. Manual trace retrieval is a time-consuming task. Automatic trace retrieval can be performed via various tools such as Information retrieval or machine learning techniques. Meanwhile, a big concern associated with automated trace retrieval is the low precision problem primarily caused by the term mismatches across documents to be traced. This study proposes an approach that addresses the term mismatch problem to obtain the greatest improvements in the trace retrieval accuracy. The proposed approach uses clustering in the automated trace retrieval process and performs an experimental evaluation against previous benchmarks. The results show that the proposed approach improves the trace retrieval precision.

show abstract

Section: Phase 2: Choosing and Applying Clustering Modelsmentioning

confidence: 99%

Clustering-based Automated Requirement Trace Retrieval

Al-walidi¹,

Azab²,

Khamis³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…The use of datasets from online social media platforms has been explored for a variety of purposes, such as gauging people's opinions [43,44], identifying topics [45,46], detecting sexist terms [47], recognising hateful speech [48], classifying text [49], and recognising entities [50]. Previous studies have also generated relevant corpora in the Hausa language for various tasks [51,52,53,25,54,55,56]. A large collection of tweets in Hausa, Igbo, Yoruba, and Nigerian Pidgin have been compiled to improve sentiment lexicons in low-resource languages [53].…”

Section: Downstream Tasks In Hausa Languagementioning

confidence: 99%

Detection and Analysis of Offensive Online Content in Hausa Language

Adam,

Zandam,

Inuwa-Dutse

2024

Preprint

View full text Add to dashboard Cite

Hausa, a major Chadic language spoken by over 100 million people in Africa, faces a challenge in the digital age. While widely used, it is considered a low-resource language from a computational linguistic perspective. This means there are limited resources and tools to analyse Hausa text, making it difficult to detect offensive and threatening language online. Our study aimed to bridge this gap. We conducted two user studies (n = 180) to understand cyberbullying in Hausa. We then created the first-ever dataset of offensive and threatening Hausa phrases to train detection systems. We developed a system to flag such content and compared it to Google translation’s ability to detect these terms. Our findings revealed a concerning trend: offensive and threatening language is prevalent online, especially in discussions about religion and politics. Our detection system was able to detect more than 70% of offensive and threatening content, although many of these were mistranslated by Google’s translation engine. We attribute this to the subtle relationship between offensive and threatening content and idiomatic expressions in the Hausa language. This highlights the importance of considering cultural nuances and idiomatic expressions in Hausa. To create a safer online environment for Hausa speakers, we recommend involving diverse stakeholders who understand local contexts and demographics. This will allow for the development of more accurate detection systems and targeted moderation strategies. Trigger Warning: Readers may find some of the terms in this study distressing or disturbing; all examples are for illustration only.

show abstract

“…The study of [13] performed sentiment analysis on English and Hausa tweets using an improved feature acquisition approach. They employed SVM, NB, and Maximum Entropy (MaxEnt) for classification.…”

Section: Related Workmentioning

confidence: 99%

“…This diversity opens up opportunities for businesses, governments, institutions, and other entities to find inferences about their entity for proper decision-making. Consequently, depending solely on sentiment analysis conducted in the English language carries a significant risk of missing crucial insights within written texts [13]. However, the increasing importance of multilingual sentiment analysis goes beyond high language limitations and becomes particularly relevant in low-resource languages.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages

Aliyu,

Sarlan,

Danyaro

et al. 2024

IJACSA

View full text Add to dashboard Cite

The analysis of sentiments expressed on social media platforms is a crucial tool for understanding user opinions and preferences. The large amount of the texts found on social media are mostly in different languages. However, the accuracy of sentiment analysis in these systems faces different challenges in multilingual low-resource settings. Recent advancements in deep learning transformer models have demonstrated superior performance compared to traditional machine learning techniques. The majority of preceding works are predominantly constructed on the foundation of monolingual languages. This study presents a comparative analysis that assesses the effectiveness of transformer models, for multilingual lowresource languages sentiment analysis. The study aims to improve the accuracy of the existing baseline performance in analyzing tweets written in 12 low-resource African languages. Four widely used start-of-the-art transformer models were employed. The experiment was carried out using standard datasets of tweets. The study showcases AfriBERTa as a robust performer, exhibiting superior sentiment analysis capabilities across diverse linguistic contexts. It outperformed the established benchmarks in both SemEval-2023 Task 12 and AfriSenti baseline. Our framework achieves remarkable results with an F1-score of 81% and an accuracy rate of 80.9%. This study provides validation of the framework's robustness in the domain of sentiment analysis across a low-resource linguistics context. our research not only contributes a comprehensive sentiment analysis framework for low-resource African languages but also charts a roadmap for future enhancements. Emphasize the ongoing pursuit of adaptability and robustness in sentiment analysis models for diverse linguistic landscapes.

show abstract

An Enhanced Feature Acquisition for Sentiment Analysis of English and Hausa Tweets

Cited by 7 publications

References 14 publications

Clustering-based Automated Requirement Trace Retrieval

Clustering-based Automated Requirement Trace Retrieval

Detection and Analysis of Offensive Online Content in Hausa Language

Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages

Contact Info

Product

Resources

About