Sentiment Analysis and Vector Embedding: A Comparative Study

Abstract

Automatic intent/sentiment classification can be done with various machine learning approaches as well as methods. But the success of these techniques majorly depends on the representation of words or documents in vector space. That can be easily consumed by machine toward learning the hidden pattern of text/corpus. To achieve this, various methods have been proposed, and many are commercially accepted as well. In deep learning architecture for intent/sentiment analysis, the vector embedding plays a crucial role. It represents feature extraction. In this paper, various methods for vector embedding are discussed along with their comparison.