Open Access

ARTICLE

Deep Learning Enabled Social Media Recommendation Based on User Comments

by K. Saraswathi^1,*, V. Mohanraj², Y. Suresh², J. Senthilkumar²

1 Department of Computer Science and Engineering, Government College of Engineering, Salem, 636011, India
2 Department of Information Technology, Sona College of Technology, Salem, 636005, India

* Corresponding Author: K. Saraswathi. Email:

Computer Systems Science and Engineering 2023, 44(2), 1691-1702. https://doi.org/10.32604/csse.2023.027987

Received 30 January 2022; Accepted 02 March 2022; Issue published 15 June 2022

Abstract

Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

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Keywords

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