Hybrid Multichannel-Based Deep Models Using Deep Features for Feature-Oriented Sentiment Analysis

Waqas Ahmad, Hikmat Ullah Khan (), Tasswar Iqbal, Muhammad Attique Khan (), Usman Tariq and Jae-hyuk Cha
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Waqas Ahmad: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
Hikmat Ullah Khan: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
Tasswar Iqbal: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
Muhammad Attique Khan: Department of Computer Science, HITEC University, Taxila 47080, Pakistan
Usman Tariq: Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Jae-hyuk Cha: Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea

Sustainability, 2023, vol. 15, issue 9, 1-26

Abstract: With the rapid growth of user-generated content on social media, several new research domains have emerged, and sentiment analysis (SA) is one of the active research areas due to its significance. In the field of feature-oriented sentiment analysis, both convolutional neural network (CNN) and gated recurrent unit (GRU) performed well. The former is widely used for local feature extraction, whereas the latter is suitable for extracting global contextual information or long-term dependencies. In existing studies, the focus has been to combine them as a single framework; however, these approaches fail to fairly distribute the features as inputs, such as word embedding, part-of-speech (PoS) tags, dependency relations, and contextual position information. To solve this issue, in this manuscript, we propose a technique that combines variant algorithms in a parallel manner and treats them equally to extract advantageous informative features, usually known as aspects, and then performs sentiment classification. Thus, the proposed methodology combines a multichannel convolutional neural network (MC-CNN) with a multichannel bidirectional gated recurrent unit (MC-Bi-GRU) and provides them with equal input parameters. In addition, sharing the information of hidden layers between parallelly combined algorithms becomes another cause of achieving the benefits of their combined abilities. These abilities make this approach distinctive and novel compared to the existing methodologies. An extensive empirical analysis carried out on several standard datasets confirms that the proposed technique outperforms the latest existing models.

Keywords: sentiment analysis; aspect extraction; word embedding; attention mechanism; contextual positional information; multichannel convolutional neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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