Open Access

ARTICLE

Deep Sentiment Learning for Measuring Similarity Recommendations in Twitter Data

S. Manikandan^1,*, P. Dhanalakshmi², K. C. Rajeswari³, A. Delphin Carolina Rani⁴

1 Department of Information Technology, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India
2 Department of CSSE, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India
3 Department of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu, India
4 Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Samayapuram, Tiruchirappalli, Tamil Nadu, India

* Corresponding Author: S. Manikandan. Email:

Intelligent Automation & Soft Computing 2022, 34(1), 183-192. https://doi.org/10.32604/iasc.2022.023469

Received 09 September 2021; Accepted 29 December 2021; Issue published 15 April 2022

Abstract

The similarity recommendation of twitter data is evaluated by using sentiment analysis method. In this paper, the deep learning processes such as classification, clustering and prediction are used to measure the data. Convolutional neural network is applied for analyzing multimedia contents which is received from various sources. Recurrent neural network is used for handling the natural language data. The content based recommendation system is proposed for selecting similarity index in twitter data using deep sentiment learning method. In this paper, sentiment analysis technique is used for finding similar images, contents, texts, etc. The content is selected based on repetitive comments and trending information. Hash tag is also considered for data collection and prediction. The number tweets are accountable and each character is taken for evaluation. Deep belief network is generated using 512 × 512 × 3 layers system and 1056 trained data, 512 test data that are taken for convolution process. The deep belief network is generated using TensorFlow. TensorFlow is used to simulate the deep learning environments. Semantic analysis is applied for handling Twitter Data. The deep learning processes are classified into clustering, regression and prediction that are evaluated by step by setup approach. The experiments are carried out using similarity index calculation and measuring of accuracy. The results of similarity recommendation are compared with existing method and the results are recorded. Our proposed system gives better results comparing with existing experiments.

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Keywords

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