{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:08:19Z","timestamp":1771614499725,"version":"3.50.1"},"reference-count":48,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target\u2019s sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Na\u00efve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification.<\/jats:p>","DOI":"10.3233\/jifs-230246","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T12:22:09Z","timestamp":1684844529000},"page":"1539-1565","source":"Crossref","is-referenced-by-count":3,"title":["Intelligence system for sentiment classification with deep topic embedding using N-gram based topic modeling"],"prefix":"10.1177","volume":"45","author":[{"given":"E.S.","family":"Smitha","sequence":"first","affiliation":[{"name":"Department of Information Science & Technology, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, India"}]},{"given":"S.","family":"Sendhilkumar","sequence":"additional","affiliation":[{"name":"Department of Information Science & Technology, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, India"}]},{"given":"G.S.","family":"Mahalakshmi","sequence":"additional","affiliation":[{"name":"Deparment of Computer Science & Technology, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, India"}]}],"member":"179","reference":[{"issue":"10","key":"10.3233\/JIFS-230246_ref1","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TSMC.2018.2875163","article-title":"A Fuzzy Decision Support Model with Sentiment Analysis for Items Comparison in e-Commerce: The Case Study of http:\/\/PConline.com","volume":"49","author":"Ji","year":"2019","journal-title":"in IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"4","key":"10.3233\/JIFS-230246_ref2","doi-asserted-by":"crossref","first-page":"819","DOI":"10.21275\/v5i4.NOV162724","article-title":"Sentiment classification using machine learning techniques","volume":"5","author":"Wawre","year":"2016","journal-title":"International Journal of Science and Research (IJSR)"},{"issue":"1","key":"10.3233\/JIFS-230246_ref3","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1609\/aaai.v30i1.9974","article-title":"Context-sensitive twitter sentiment classification using neural network","volume":"30","author":"Ren","year":"2016","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"05","key":"10.3233\/JIFS-230246_ref4","doi-asserted-by":"crossref","first-page":"9177","DOI":"10.1609\/aaai.v34i05.6454","article-title":"Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning","volume":"34","author":"Wang","year":"2020","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-230246_ref6","doi-asserted-by":"crossref","first-page":"66509","DOI":"10.1109\/ACCESS.2021.3077059","article-title":"Deep Learning Sentiment Classification Based on Weak Tagging","volume":"9","author":"Wang","year":"2021","journal-title":"Information. 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