Analysis of Text-Semantics via Efficient Word Embedding using Variational Mode Decomposition.

In this paper, we propose a novel method which establishes a newborn relation between Signal Processing and Natural Language Processing (NLP) method via Variational Mode Decomposition (VMD). Unlike the modern Neural Network approaches for NLP which are complex and often masked from the end user, our approach involving Term Frequency Inverse Document Frequency (TFIDF) aided with VMD dials down the complexity retaining the performance with transparency. The performance in terms of Machine Learning based approaches and semantic relationships of words along with the methodology of the above mentioned approach is analyzed and discussed in this paper.