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The ensemble of Sentic computing and supervised learning to perform TSA was investigated in the literature. However, these techniques require labelled data for training. This study investigated an unsupervised framework based on the ensemble (through delegation) of Sentic computing and clustering. A novel clustering optimisation algorithm is proposed using combined proximity measures to optimise Tweets\u2019 clusters. The proposed framework eliminates the need for labelled data which are required in the case of supervised learning. Four Twitter datasets are used to evaluate the proposed framework and proposed novel clustering optimisation algorithm. The area under the curve has been considered for evaluation. The experiments elucidate that the proposed ensemble framework with integration of novel clustering optimisation algorithm could be a better choice for TSA when compared to a traditional supervised setup.<\/jats:p>","DOI":"10.1142\/s0219649225500728","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T14:45:52Z","timestamp":1756305952000},"source":"Crossref","is-referenced-by-count":0,"title":["ICAIMT: Twitter Sentiment Analysis Using Ensemble of Sentic Computing and a Novel Clustering Optimisation Algorithm Based on Combined Proximity Measures"],"prefix":"10.1142","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9619-8565","authenticated-orcid":false,"given":"Maryum","family":"Bibi","sequence":"first","affiliation":[{"name":"The University of Azad Jammu & Kashmir, Muzaffarabad, Azad Kashmir, 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