{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:22:29Z","timestamp":1773030149525,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":313,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Social media platforms, such as X (formerly Twitter), provide users with concise but impactful tools to express their views and feelings. Users present their views and express their feelings in hashtags and emojis on a wide range of topics. The sheer volume of this textual data offers a rich source for analyzing public sentiment and emotions. Numerous machine learning and deep learning approaches have been presented lately for optimal emotion detection and sentiment analysis of these tweets. Given the complexity of processing human language, natural language processing (NLP) techniques face the challenge of explainability in their decision\u2010making process. To bridge this gap, we introduce an explainable NLP\u2010based framework for the recognition of human emotions within textual data. We propose a novel recurrent neural network architecture incorporating a bidirectional long short\u2010term memory layer for emotion prediction and sentiment analysis on English tweets. The performance of the proposed model is evaluated with real\u2010world X data against benchmark techniques. The proposed model achieves accuracy, precision, recall, and an F1\u2010score of over 90%, which is higher than the considered benchmark models. Subsequently, we integrate the explainable artificial intelligence (XAI) approaches, namely, local interpretable model\u2010agnostic explanations (LIME) and SHapely Additive exPlanation (SHAP) to explain the decision\u2010making process behind the proposed model\u2019s prediction. Applying these XAI techniques not only boosts the proposed model\u2019s transparency but also reinforces its reliability in accurately processing and explaining textual data.<\/jats:p>","DOI":"10.1155\/cplx\/9258956","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:47:31Z","timestamp":1762786051000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable AI Models for Decoding Emotional Subtexts on Social Media"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9186-6392","authenticated-orcid":false,"given":"Dost","family":"Muhammad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7863-3746","authenticated-orcid":false,"given":"Iftikhar","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4972-6630","authenticated-orcid":false,"given":"Khwaja","family":"Naveed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0069-1860","authenticated-orcid":false,"given":"Malika","family":"Bendechache","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2010.05.046"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.5840\/philtopics20134116"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iheduc.2011.10.001"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1108\/10444060810909301"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10033-z"},{"key":"e_1_2_12_6_2","article-title":"A Review on Evaluation Metrics for Data Classification Evaluations","volume":"5","author":"Hossin M.","year":"2015","journal-title":"International Journal of Data Mining & Knowledge Management Process"},{"key":"e_1_2_12_7_2","doi-asserted-by":"crossref","unstructured":"El RahmanS. 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