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• W2964028220 abstract "Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing." @default.
• W2964028220 created "2019-07-30" @default.
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• W2964028220 date "2019-07-01" @default.
• W2964028220 modified "2023-10-06" @default.
• W2964028220 title "Adam Deep Learning With SOM for Human Sentiment Classification" @default.
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• W2964028220 doi "https://doi.org/10.4018/ijaci.2019070106" @default.
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