{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:22:13Z","timestamp":1777706533264,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,9,22]]},"abstract":"<jats:p>\u00a0Many websites are attempting to offer a platform for users or customers to leave their reviews and comments about the products or services in their native languages. The cross-domain adaptation (CDA) analyses sentiment across domains. The sentiment lexicon falls short resulting in issues like feature mismatch, sparsity, polarity mismatch and polysemy. In this research, an augmented sentiment dictionary is developed in our native regional language (Tamil) that intends to construct the contextual links between terms in multi-domain datasets to reduce problems like polarity mismatch, feature mismatch, and polysemy. Data from the source domain and target domain both labeled and unlabeled are used in the proposed dictionary. To be more specific, the initial dictionary uses normalised pointwise mutual information (nPMI) to derive contextual weight, whereas the final dictionary uses the value of terms across all reviews to compute the accurate rank score. Here, a deep learning model called BERT is used for sentiment classification. For cross-domain adaptation, a modified multi-layer fuzzy-based convolutional neural network (M-FCNN) is deployed. This work aims to build a single dictionary using large number of vocabularies for classifying the reviews in Tamil for several target domains. This extendible dictionary enhances the accuracy of CDA greatly when compared to existing baseline techniques and easily handles a large number of terms in different domains.<\/jats:p>","DOI":"10.3233\/jifs-220448","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T10:56:57Z","timestamp":1657882617000},"page":"6433-6450","source":"Crossref","is-referenced-by-count":6,"title":["Sentiment lexicon for cross-domain adaptation with multi-domain dataset in Indian languages enhanced with BERT classification model"],"prefix":"10.1177","volume":"43","author":[{"given":"K.","family":"Suresh Kumar","sequence":"first","affiliation":[{"name":"IFET College of Engineering (Autonomous), Villupuram, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Helen Sulochana","sequence":"additional","affiliation":[{"name":"St. Xavier\u2019s Catholic College of Engineering, Kanyakumari, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.S.","family":"Radhamani","sequence":"additional","affiliation":[{"name":"Amrita College of Engineering & Technology, Nagerkoil, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Ananth Kumar","sequence":"additional","affiliation":[{"name":"IFET College of Engineering (Autonomous), Villupuram, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-220448_ref1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.procs.2021.12.006","article-title":"Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products development or positioning,} }","volume":"196","author":"Jardim","year":"2022","journal-title":"Procedia Computer Science"},{"key":"10.3233\/JIFS-220448_ref2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.inffus.2016.10.004","article-title":"A review of natural language processing techniques for opinion mining systems","volume":"36","author":"Sun","year":"2017","journal-title":"Information Fusion"},{"key":"10.3233\/JIFS-220448_ref3","doi-asserted-by":"crossref","first-page":"107658","DOI":"10.1016\/j.patcog.2020.107658","article-title":"Unified Cross-domain Classification via Geometric and Statistical Adaptations","volume":"110","author":"Liu","year":"2021","journal-title":"Pattern Recognition"},{"issue":"2","key":"10.3233\/JIFS-220448_ref4","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s10462-020-09884-9","article-title":"360 degree view of cross-domain opinion classification: a survey","volume":"54","author":"Singh","year":"2021","journal-title":"Artificial Intelligence Review"},{"key":"10.3233\/JIFS-220448_ref5","doi-asserted-by":"crossref","first-page":"16173","DOI":"10.1109\/ACCESS.2017.2690342","article-title":"Approaches to cross-domain sentiment analysis: A systematic literature review","volume":"5","author":"Al-Moslmi","year":"2017","journal-title":"IEEE Access"},{"issue":"1","key":"10.3233\/JIFS-220448_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13278-022-00877-w","article-title":"Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets","volume":"12","author":"Patil","year":"2022","journal-title":"Social Network Analysis and Mining"},{"key":"10.3233\/JIFS-220448_ref9","doi-asserted-by":"crossref","unstructured":"Sumathy B. , Kumar A. , Sungeetha D. , Hashmi A. , Saxena A. and Kumar P. , Shukla and S.J. 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