Improving the Integration and Dynamic of Sentiment Analysis Prediction using Fast Vector Space Model

Abstract

Many previously trained language models have been included, and the sentiment analysis function has been enhanced. This paper proposes a technique for predicting feelings that includes a supporting phrase explaining the characteristics in the sentence. The first is feature detection, which employs a multi-dimensional model to anticipate all characteristics of a sentence. Sentiment Analysis is a technique for modelling that combines predicted characteristics with the initial phrase. There is often a lack of domain data identified for optimization due to the costly definition of the word element. Many approaches to transmit common information in an uncontrolled manner have lately been suggested to overcome this challenge, however such systems have too many modules and need pre-processing of many costly categories. The strategy proposed in this study is basic yet effective. It focused on improving integrated data, which may be used as a part of the development of any sort of cross-platform model.