Gender bias in sentiment analysis

Thelwall, Mike

doi:10.1108/oir-05-2017-0139

Cited by 62 publications

(60 citation statements)

References 27 publications

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“…Although the reasons for the differential use of language between men and women needs further investigations 78 , the existence of such differences can either facilitate or complicate the development of NLP technologies. For instance, while it is possible to accurately categorize texts based on the author's gender 79 , performances of sentiment analysis of male-and female-authored texts are extremely variable 80 and potentially biased 81 . Thus, knowing the sex and gender of the author enables a better targeted prediction of symptoms conveyed through natural language (text or speech).…”

Section: Natural Language Processingmentioning

confidence: 99%

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

et al. 2020

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Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.

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Section: Natural Language Processingmentioning

confidence: 99%

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

et al. 2020

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“…Existing opinion annotations schemes (i.e., OpinionMining-ML, EmotionML and SentiML) fail to deal with many situations which, if annotated well, could be influential for developing better opinion mining systems. Problems like contextual ambiguities [6,7], lack of semantics interpretation on sentence level, tackling temporal expressions [8,9], identification of opinion holders [10][11][12], opinion aggregation and their comparison [13,14] remain unanswered by these annotations. Each of the opinion annotation schemes have positive and negative features associated with them but there is a need to have a strong opinion annotation which combines positive features of existing schemes (like flexible emotion vocabulary choice in EmotionML, feature-level processing of OpinionMining-ML, etc.)…”

Section: Motivation and Contributionmentioning

confidence: 99%

OpinionML—Opinion Markup Language for Sentiment Representation

Attik¹,

Missen

Coustaty

et al. 2019

Symmetry

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It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

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“…The influence of bias in data can be noticed in numerous applications. There is a known problem of gender and racial bias in sentiment analysis [23]. It appears that certain groups of people seem to be using specific words more often than others.…”

Section: Bias In Datamentioning

confidence: 99%

Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data

Kwasigroch

2020

Journal of Artificial Intelligence and Soft Computing Research

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The paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results are validated on a sample skin lesion dataset. Using the proposed method, a number of possible bias-causing artifacts are successfully identified and confirmed in dermoscopy images. In particular, it is confirmed that black frames have a strong influence on Convolutional Neural Network’s prediction: 22% of them changed the prediction from benign to malignant.

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Gender bias in sentiment analysis

Cited by 62 publications

References 27 publications

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

OpinionML—Opinion Markup Language for Sentiment Representation

Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data

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