An efficient two-state GRU based on feature attention mechanism for sentiment analysis

Zulqarnain, Muhammad; Ghazali, Rozaida; Aamir, Muhammad; Hassim, Yana Mazwin Mohmad

doi:10.1007/s11042-022-13339-4

Cited by 26 publications

(9 citation statements)

References 34 publications

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“…This investigation clearly identified the difficulties covered in the next section. Preserving the authenticity of the guidelines maintaining the integrity of the specifications [7][8][9].…”

Section: Challenges In Opinion Extraction and Sentiment Analysismentioning

confidence: 99%

A Review on Challenges in Recent Opinion Extraction Techniques

Kumar,

Ahamad

2024

International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

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With the introduction of Web 2.0, people are now encouraged to share information about events in their community as well as to voice their opinions and beliefs. People utilize social media, blogs, review sites, news sites, user feedback portals, and other platforms to express themselves regarding events, locations, decisions made by leaders, policies, and other topics. Opinion extraction is a method that automatically extracts and analyzes people's sentiments from their thoughts. Manufacturers may find this collected data helpful in analyzing their products. Sentiment analysis, also referred to as opinion extraction, has been the subject of intense research over the past 15 years by academics, research communities, and service corporations in an effort to gather and examine public opinions and moods. This work focuses on the difficulties encountered in opinion extraction at many levels, including word-, sentence-, and language-related difficulties as well as some generic difficulties. The study provided clear examples and a thorough discussion of 30 hurdles, which aid in presenting problems for text or opinion extraction research. Opinion extraction tools are briefly presented in this section to help novice researchers get started. The published material spans the years 2004 to 2022.

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Section: Challenges In Opinion Extraction and Sentiment Analysismentioning

confidence: 99%

A Review on Challenges in Recent Opinion Extraction Techniques

Kumar,

Ahamad

2024

International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

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“…The model lacks stability issues. Zulqarnain et al [ 22 ] presented a two-state GRU (TS-GRU) depending on the feature attention process, focusing on word-feature capturing and sequential modeling to discover and classify the sentiment polarity. The TS-GRU approach could be more computationally challenging.…”

Section: Literature Reviewmentioning

confidence: 99%

LSTM‐DGWO‐Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

Barik¹,

Misra

Ray³

et al. 2023

Computational Intelligence and Neuroscience

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Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer’s perception of enhancing products from a business perspective.

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“…The Gated Recurrent Unit, initially introduced by Chung et al [39], tackles the prevalent problem of lengthy contextual connections that often result in gradient degradation within conventional, extensive RNN networks. This innovation has since evolved into a modern architecture, referred to as the "two gated mechanism" approach.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Text Classification Using Deep Learning Models: A Comparative Review

Muhammad Zulqarnain,

Rubab Sheikh,

Shahid Hussain

et al. 2023

Cloud Computing and Data Science

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With the fast popularization and continued development of web pages on the Internet, text classification has become a very serious problem in organizing and managing large amounts of digital text data in documents. The deep learning approaches have been applied in several areas of text classification with comparative and outstanding results. In this article, we analyzed and gave comprehensive reviews of the different deep learning models for text classification tasks. Based on the literature review survey, this paper addresses three various deep learning models and declares their gaps and limitations. We have evaluated the various classification applications and a small discussion on the available Deep Neural Networks (DNN) frameworks for the implementation of text datasets. The work presents guidance for future research to regulate more significance that can be distributed for the better area of this research. In summary, our study presented the main implications, identified potential directions for future research, and highlighted the challenges within this specific research field. Additionally, our aim is to acquaint readers with the various subtasks and relevant literature related to the text classification process. By engaging with our discussion, we aspire to inspire readers to explore novel and enhanced techniques for text classification, applicable across diverse domains.

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An efficient two-state GRU based on feature attention mechanism for sentiment analysis

Cited by 26 publications

References 34 publications

A Review on Challenges in Recent Opinion Extraction Techniques

A Review on Challenges in Recent Opinion Extraction Techniques

LSTM‐DGWO‐Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

Text Classification Using Deep Learning Models: A Comparative Review

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