Leveraging Deep Learning for Improved Sentiment Analysis in Natural Language Processing

Sentiment analysis is viewed as quite
possibly of the main works in mental science and
normal language handling. To work on the
productivity of sentiment analysis techniques, it is
crucial for separate the useful words that add to the
grouping choice as well as characterize the sentences
as indicated by their profound names. Profound
brain networks that depend on the consideration
component have taken huge steps toward this path as
of late. Reads up on consideration processes for
message arrangement, and especially sentiment
analysis, are as yet not many, in any case. This
research fills this gap by presenting a Convolution
Neural Network (CNN) combined with an attention
layer that can extract relevant words and give them
greater weights according to the context. The
suggested model uses a context vector at the attention
layer and attempts to gauge a word's relevance based
on how similar the word vector and context vector
are to one another. New vectors from the
consideration layer are incorporated into sentence
vectors and utilised for organization once they have
been supplied. The suggested model was validated
using a small number of tests on the Stanford
datasets. The trial discoveries show that the
recommended model works far superior to past
research studies and can separate significant
expressions from setting that have an incentive for
analysis and application.