University of Bahrain
Scientific Journals

Deep Sentiment Approaches for Rigorous Analysis of Social Media Content & Its Investigation

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dc.contributor.author Kumar Sharma, Amit
dc.contributor.author Chaurasia, Sandeep
dc.contributor.author Kumar Srivastava, Devesh
dc.date.accessioned 2021-08-23T00:41:29Z
dc.date.available 2021-08-23T00:41:29Z
dc.date.issued 2021-08-23
dc.identifier.issn 2210-142X
dc.identifier.uri https://journal.uob.edu.bh:443/handle/123456789/4512
dc.description.abstract Social media has a very important contribution to human lives today. Through social media platforms people can share their information, ideas, knowledge, and activities with connecting people in the form of videos, images, texts, and audios. In the context of sharing information, incorrect information is also shared along with the correct information. In this way, unauthentic (fake news), misleading (rumors), abusing, toxic, extremist contents are also shared through social media platforms. This paper reviews the influences of social media contents. In this context, vector representation of the social media sentences, word embedding models has been best applied for better accurate results. Natural language processing (NLP) and text analysis techniques is being used to extract useful information from social media content. The NLP techniques are widely used for correcting the sentences and identifying their meaning also. Currently, machine learning (Decision Tree, Random Forest, SVM, Naïve Bayes) and deep learning (LSTMs, BLSTMs, GRUs, CNNs) models are successfully being implemented to classify social media contents. In the comparative study of different works of literature and results from LSTM deep learning model have been proved that deep learning and the word embedding model provide better accurate results for social media contents categorization. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Deep Learning en_US
dc.subject Machine Learning en_US
dc.subject NLP, Rumors en_US
dc.subject Social Media en_US
dc.subject Text Analysis en_US
dc.subject Word Embedding en_US
dc.title Deep Sentiment Approaches for Rigorous Analysis of Social Media Content & Its Investigation en_US
dc.identifier.doi https://dx.doi.org/10.12785/ijcds/120116
dc.pagestart 171
dc.pageend 185
dc.contributor.authorcountry India en_US
dc.contributor.authoraffiliation Department of Computer Science and Engineering, Manipal University Jaipur en_US
dc.source.title International Journal Of Computing and Digital System en_US
dc.abbreviatedsourcetitle IJCDS en_US


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