An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis - Srinidhi, K; L.S Tejaswi, T; Rama Rupesh Kumar, CH; Sai Siva Charan, I (International Journal of Engineering and Technology, 2018)
We propose an advanced well-trained sentiment analysis based adoptive analysis â€œword specific embeddingâ€™s, dubbed sentiment embeddingâ€™sâ€. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social networking sites we developed neural network based algorithms along with tailoring and loss function which carry feelings. This research apply embeddingâ€™s to word-level, sentence-level sentimental analysis and classification, constructing sentiment oriented lexicons. Experimental analysis and results addresses that sentiment embedding techniques outperform the context-based embeddingâ€™s on many distributed data sets. This work provides familiarity about neural networks techniques for learning word embeddingâ€™s in other NLP tasks.Â