Gender bias in machine learning for sentiment analysis

Thelwall, Mike

doi:10.1108/oir-05-2017-0153

Cited by 17 publications

(15 citation statements)

References 35 publications

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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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Section: Motivation and Contributionmentioning

confidence: 99%

OpinionML—Opinion Markup Language for Sentiment Representation

Attik¹,

Missen

Coustaty

et al. 2019

Symmetry

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“…Recent literature has questioned the promises of algorithms’ objectivity (Bilić 2016; Porter 1996; Thelwall 2018). For example, O’Neil (2016) has suggested, drawing from her own experience as a mathematician in finance, that an algorithm used for HR processes, such as employee recruitment, evaluation, or performance appraisals, are still impaired by racial and gender biases.…”

Section: Algorithm-based Decision-making: Objective Unbiased and Efmentioning

confidence: 99%

The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity

et al. 2019

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Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility.

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“…We employ multiple cross-domain datasets from the English language, IMDB (Maas et al, 2011), Yelp 5 , TripAdvisor (Thelwall, 2018), Clothing 6 , UCI Drug 7 , WebMD 8 as shown in Table 1. We train the Logistic Regression (LR) classifier using cross-domain datasets and use the trained model to predict the semantic orientations of reviews in our machine-translated corpus.…”

Section: Transfer Learning-based Approachmentioning

confidence: 99%

Cross-lingual sentiment classification in low-resource Bengali language

Sazzed¹

2020

Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-Nut 2020)

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Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools. Therefore, in this work, we focus on generating resources and showing the applicability of the crosslingual sentiment analysis approach in Bengali. For benchmarking, we created and annotated a comprehensive corpus of around 12000 Bengali reviews. To address the lack of standard text-processing tools in Bengali, we leverage resources from English utilizing machine translation. We determine the performance of supervised machine learning (ML) classifiers in machine-translated English corpus and compare it with the original Bengali corpus. Besides, we examine sentiment preservation in the machine-translated corpus utilizing Cohen's Kappa and Gwet's AC1. To circumvent the laborious data labeling process, we explore lexicon-based methods and study the applicability of utilizing cross-domain labeled data from the resource-rich language. We find that supervised ML classifiers show comparable performances in Bengali and machinetranslated English corpus. By utilizing labeled data, they achieve 15%-20% higher F1 scores compared to both lexicon-based and transfer learning-based methods. Besides, we observe that machine translation does not alter the sentiment polarity of the review for most of the cases. Our experimental results demonstrate that the machine translation based crosslingual approach can be an effective way for sentiment classification in Bengali.

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Gender bias in machine learning for sentiment analysis

Cited by 17 publications

References 35 publications

OpinionML—Opinion Markup Language for Sentiment Representation

OpinionML—Opinion Markup Language for Sentiment Representation

The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity

Cross-lingual sentiment classification in low-resource Bengali language

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