Applying Character-Level Neural Network-Based Sentiment Analysis Model on Persian Comments of the Social Media-Online Store Platforms

Articles in Press

Document Type : Original Article

Authors

1 Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran

2 Department of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran

Abstract

Nowadays, due to people being more willing to shop online through online stores and social media, we are facing the growth of unstructured data like texts on the internet. Hence, text processing and the development of optimal algorithms for extracting knowledge have drawn scholar’s attention to this field. One of the aspects of the text processing field is classifying texts in the form of classes of various sentiments using different algorithms. We propose a novel framework to classify the comments based on the user’s sentiment performed in the character-level scenario. Hence, the proposed framework is mounted on the architecture of embedding from the language model triggered by the quad-layer, namely embedding, one-dimensional convolution, the map, and the recurrent neural network. In the proposed framework, first, by using the embedding layer at the level of the character, a constant vector is assigned to them. Next, the semantic and logical relation between the characters per word for surviving word-specific 128-dimensional vectors is extracted by exerting the parallel-oriented one-dimensional convolution operators. After obtaining vectors, based on two recurrent neural network architectures, the relationship between the discovered words and the comment-specific sentiment is determined. The obtained results show that the proposed framework has an Accuracy of 79.87% and a F-score of 79.9% for comments class labeling.

Keywords

Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 08 May 2023

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History

  • Receive Date: 15 September 2022
  • Revise Date: 21 March 2023
  • Accept Date: 06 May 2023

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