MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis

The fast improvement and transformation of online media and unique sites with critical reviews of items, movies, goods, etc. have created a tremendous assortment of assets for clients everywhere around the globe. This information might contain a great deal of data, including product reviews, anticipating market changes, and the extremity of film assessments. Sentiment Analysis (SA) innovation produces phonetic comprehension according to the viewpoint of machines through the handling and investigation of immense amounts of information, which is a hot expedition passageway heading into the field of man-made reasoning, a.k.a. Artificial Intelligence (AI). To address the substance appendage from short texts, we want to investigate the further semantics of words by exploiting thoughtful Machine Learning (ML) and Deep Learning (DL) strategies. In this way, AI, ML, and DL procedures can control and distribute intuition introspection in these difficulties. Our recommended model, based on the DL method and the GloVe word embedding approach, learns the features using a CNN layer and then coordinates those parts into a Multi-Layered Bi-DirectionalLong-Short-Term Memory (MBiLSTM) to capture long-range embedded circumstances. The main aim of this experiment is to give an adequate answer to examine feelings and user reviews in positive and negative classifications. Our runs show that a test accuracy of 92.05% and a validation accuracy of 93.55% can be attained with the given model. The framework is assessed using IMDB datasets. The proposed model outflanks existing pattern models, which show that going past the substance of a tweet is valuable in opinion classification orders since it gives the classifier a deep understanding of the chore.