AI for Social Good: Sentiment Analysis to Detect Social Challenges in South Africa

Mabokela, Koena Ronny; Schlippe, Tim

doi:10.1007/978-3-031-22321-1_21

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The extended framework was proposed to work in the following manner: First, news articles are collected using dynamic web scraping on online news websites. For Twitter data, we used Twitter API to collect tweets using the geo-location-based data collection feature with special keywords and hashtags (Mabokela & Schlippe, 2022). The date range of the collected news articles and tweets ranged from 2014 to 2020.…”

Section: Data Acquisitionmentioning

confidence: 99%

Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Koena,

Primus,

Mahlobo

2024

JDHASA

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This study investigates the prevalence of evictions in South Africa and examines potential disparities between traditional media reporting and social media discourse. Employing a sentiment analysis framework, we extend its application to compare the reporting of evictions in newspaper articles (i.e. conventional media) and Twitter data (i.e. social media). Statistical machine-learning methods are utilized to predict sentiment scores for both types of content, and a chi-square test is employed to evaluate bias between news articles and tweets. The test results reveal a significant bias in the sentiment distribution, suggesting that the dissimilarities observed between articles and tweets are not merely coincidental.

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Section: Data Acquisitionmentioning

confidence: 99%

Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Koena,

Primus,

Mahlobo

2024

JDHASA

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“…This field has received a lot of attention which led to its numerous successful NLP technologies in various areas (Aguero-Torales et al, 2021;. For example, its popular application has been in social media monitoring, support management, customer feedback (Wankhade et al, 2022) and AI for social good (Mabokela and Schlippe, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Investigating Sentiment-Bearing Words- and Emoji-based Distant Supervision Approaches for Sentiment Analysis

Mabokela,

Roborife,

Celik

2023

Proceedings of the Fourth Workshop on Resources for African Indigenous Languages (RAIL 2023)

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Sentiment analysis focuses on the automatic detection and classification of opinions expressed in texts. Emojis can be used to determine the sentiment polarities of the texts (i.e. positive, negative, or neutral). Several studies demonstrated how sentiment analysis is accurate when emojis are used (Kaity and Balakrishnan, 2020). While they have used emojis as features to improve the performance of sentiment analysis systems, in this paper we analyse the use of emojis to reduce the manual effort in labelling text for training those systems. Furthermore, we investigate the manual effort reduction in the sentiment labelling process with the help of sentiment-bearing words as well as the combination of sentiment-bearing words and emojis. In addition to English, we evaluated the approaches with the low-resource African languages Sepedi, Setswana, and Sesotho. The combination of emojis and words sentiment lexicon shows better performance compared to emojis-only lexicons and words-based lexicons. Our results show that our emoji sentiment lexicon approach is effective, with an accuracy of 75% more than other sentiment lexicon approaches, which have an average accuracy of 69.1%. Furthermore, our distant supervision method obtained an accuracy of 77.0%. We anticipate that only 23% of the tweets will need to be changed as a result of our annotation strategies.

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Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT

Mindner,

Schlippe,

Schaaff

2023

Lecture Notes on Data Engineering and Communications Technologies

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AI for Social Good: Sentiment Analysis to Detect Social Challenges in South Africa

Cited by 4 publications

References 21 publications

Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Investigating Sentiment-Bearing Words- and Emoji-based Distant Supervision Approaches for Sentiment Analysis

Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT

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