{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:49:12Z","timestamp":1764780552708,"version":"3.46.0"},"reference-count":15,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["AI Matters"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p>Natural Language Processing (NLP) models have seen impressive advancements in understanding word associations. Still, limited attention has been given to user sentiment and emotions influencing these associations. In this paper, we explore the impact of both sentiment and emotion on the selection of words in an N-gram-based word association game. By integrating GloVe embeddings with SenticNet-based emotion classification and sentiment analysis from VADER, we evaluate how positive and negative sentiments, combined with intense emotions such as delight, ecstasy, terror, and loathing, affect word choices. Our findings suggest that user sentiment and emotion have a significant effect on word selection, with positive players associating positive adjectives with positive nouns, while negative players tend toward the opposite associations. This study highlights the necessity of considering sentiment and emotional intelligence in future NLP systems and presents new applications in areas such as AI-based gaming, behavioral analysis, and human-computer interaction.<\/jats:p>","DOI":"10.1145\/3774399.3774407","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:45:18Z","timestamp":1764780318000},"page":"49-58","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An N-Gram Framework for Sentiment and Emotion-Aware Word Association Games"],"prefix":"10.1145","volume":"11","author":[{"given":"Rohan","family":"Dalal","sequence":"first","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjana","family":"Menon","sequence":"additional","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sohan","family":"Hajra","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy","family":"Blum","sequence":"additional","affiliation":[{"name":"Pennsylvania State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Kaur Sumandeep Geeta Sikka and Lalit Kumar Awasthi. 2018. \"Sentiment Analysis Approach Based on N-Gram and KNN Classifier.\" In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) 38:1\u20134. 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SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives. The COLING 2016 Organizing Committee, pages 2666\u20132677."},{"key":"e_1_2_1_10_1","volume-title":"Adjectives List [Data set]. www.kaggle.com\/datasets\/jordansiem\/adjectives-list","author":"J.","year":"2019","unstructured":"Siem, J. (2019). Adjectives List [Data set]. www.kaggle.com\/datasets\/jordansiem\/adjectives-list"},{"key":"e_1_2_1_11_1","volume-title":"List of Nouns [Data set]. www.kaggle.com\/datasets\/leite0407\/list-of nouns","author":"M.","year":"2019","unstructured":"Leite, M. (2019). List of Nouns [Data set]. www.kaggle.com\/datasets\/leite0407\/list-of nouns"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1810.04805"},{"key":"e_1_2_1_13_1","volume-title":"Improving Language Understanding by Generative Pre-Training","author":"K.","year":"2018","unstructured":"Radford, A., & Narasimhan, K. (2018). Improving Language Understanding by Generative Pre-Training."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1561\/1500000011"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 2024 Conference on Affective Computing and Intelligent Interaction (ACII). College of Computing and Data Science","author":"M.","year":"2024","unstructured":"Cambria, E., Zhang, X., Mao, R., Kwok, K., & Chen, M. (2024). SenticNet 8: Fusing emotion AI and commonsense AI for interpretable, trustworthy, and explainable affective computing. In Proceedings of the 2024 Conference on Affective Computing and Intelligent Interaction (ACII). College of Computing and Data Science, Nanyang Technological University."}],"container-title":["AI Matters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774399.3774407","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:45:23Z","timestamp":1764780323000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774399.3774407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10.1145\/3774399.3774407"],"URL":"https:\/\/doi.org\/10.1145\/3774399.3774407","relation":{},"ISSN":["2372-3483"],"issn-type":[{"value":"2372-3483","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}