RETRACTED ARTICLE: Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM

Classroom teaching becomes viable and efficient based on increase in participation of the student. This can be made possible by taking needed measure by finding the emotions of the students. Many researchers worked on emotion identification of students. Now-a-days sentiment analysis using deep learning models have gained good performance. Especially ensemble Long Short-Term Memory (LSTM) with attention layers gives more attention to the influence word on the emotion. In the proposed method, input sequences of sentences are processed parallel across multi-head attention layer with fine grained embeddings (Glove and Cove) and tested with different dropout rates to increase the accuracy. Later in this paper, the information from both deep multi-layers is fused and fed as input to the LSTM layer. In this paper, we conclude that the fusion of multiple layers accompanied with LSTM improves the result over a common Natural Language Processing method.