Sentiment Analysis in Social Media Using Deep Learning Techniques
Keywords:
Twitter, sentiment analysis, deep learning, social networks, classification.Abstract
The process of analyzing feelings, views, and emotions expressed in social media content a process commonly referred to as opinion mining or sentiment analysis has grown in importance. Because social media platforms are growing at an exponential rate, a large amount of user-generated data that offers insightful information on trends, consumer behavior, and public opinion is readily available. Sentiment analysis on social networks is hampered by the inherent qualities of the Twitter language as well as the briefness and lack of context of messages on these platforms. In this study, we provide a deep learning model to detect the degree of polarity in Twitter postings using long short term memory and convolutional layers. By up to 18%, our model greatly increased the accuracy of previous methods, with an accuracy of 91%. The abundance of user generated information on social media has made sentiment analysis more crucial than ever. Sentiment analysis and comprehension of text data may now be accomplished with the help of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. Using RNN and LSTM models, this study offers a thorough review of sentiment analysis in social media. It covers the design, methods of training, difficulties encountered, and uses of RNNs and LSTMs for sentiment analysis of social media material.
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