Sentiment Analysis of Textual Data using Deep Learning

Textual data is generated in large volume everyday over internet which makes sentiment analysis important . Sentiment analysis of any textual data denotes the feelings and attitudes of the individual on particular topics or products. It extracts the Sentiment polarity (negative ,neutral or positive ) from textual data using deep learning algorithms .Sentiment analysis of textual data involves using neural network architecture to analyze and determine the sentiment expressed in text .It is a subfield of text classification which involves analyzing people's opinions , emotions , and attitudes towards entities and their characteristics as expressed in a written text . It utilizes three deep learning algorithms which are Neural Networks , Long Short Term Memory (LSTM) , and Gated Recurrent Unit (GRU). The results of RNN, LSTM, and GRU obtain an excellent rate of accuracy . It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate Keywords: These are the neural network architectures employed for sentiment analysis such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Transformer models