Sentiment Analysis of Textual Data using Deep Learning

Sentiment Analysis of Textual Data using Deep Learning

Prof.T. Ramya, J.Mary Ratna Manjari,  Kanagandula Aravind ,Dasari Aravind Sagar,

Daripally Akhilesh Goud ,Yenugula Arun,

Artificial Intelligence & MachineLearning Department Of Computer Science And Engineering Malla Reddy University, Hyderabad,Telangana,India

Abstract: 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