Interpretable Movie Review Analysis Using Machine Learning and Transformer Models Leveraging XAI

Ahmed, Farzad; Sultana, Samiha; Reza, Md Tanzim; Joy, Sajib Kumar Saha; Alam, Md. Golam Rabiul

doi:10.1109/csde56538.2022.10089294

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…In news classification, [126] have added LIME to a BERT-based classifier detecting misinformation about COVID-19, which show the users how the decision was reached which data sources were used to make the classification, extracting sentences from relative news articles to explain the classification. Similar approaches have been employed in the medical field [127] and analysis of online reviews [128].…”

Section: How Safe?mentioning

confidence: 99%

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Fields,

Chovanec,

Madiraju

2024

IEEE Access

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Text classification is a basic task in natural language processing (NLP) with applications from sentiment analysis to question-answering with chat bots. In recent years, transformer-based models have emerged as the prevailing framework in NLP, demonstrating excellent results across many benchmarks. This paper recommends an expanded taxonomy of applications and provides a review of the performance of different models across these applications. The use of traditional research techniques plus co-citation and bibliographic coupling provides a comprehensive view of the current and past research in this area. The study begins by providing an overview of the history of transformer-based models with an emphasis on recent large language models (LLM). Next, uni-modal (text only) inputs and the emerging area of multi-modal classification are discussed to provide a comparison of current and emerging research in this area. Gaps are highlighted in the use of multi-modal text/numeric/columnar data and recommendations for future research are provided. Finally, the length of text input variables (tokens) is reviewed to explore the evolution from short-text to longer document applications. Furthermore, the accuracy on 358 datasets across 20 applications is reviewed and unexpected results emerge which show that LLMs are not always the most accurate or least expensive option. In addition to model performance, the safety implications of transformer-based models are reviewed, and a summary of issues related to ethics, bias, social implications, and copyright are explored.

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Section: How Safe?mentioning

confidence: 99%

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Fields,

Chovanec,

Madiraju

2024

IEEE Access

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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Hassija,

Chamola,

Mahapatra

et al. 2023

Cogn Comput

226

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Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy.

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The Impact of Explainable AI on Low-Accuracy Models: A Practical Approach with Movie Genre Prediction

Kerger

2024

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)

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Interpretable Movie Review Analysis Using Machine Learning and Transformer Models Leveraging XAI

Cited by 4 publications

References 18 publications

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

The Impact of Explainable AI on Low-Accuracy Models: A Practical Approach with Movie Genre Prediction

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