Research on non-dependent aspect-level sentiment analysis

Jiang, Lei; Li, Yuan; Liao, Jing; Zou, Ziwei; Jiang, Congqing

doi:10.1016/j.knosys.2023.110419

Cited by 16 publications

(4 citation statements)

References 9 publications

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“…Several recent studies have presented solutions for extracting sentiments at an aspect level from input texts. Jiang et al (2023a) [5] introduced a solution for aspect-level sentiment analysis utilizing the Gated Convolutional Network with Aspect Embedding (GCAE) [6] model. This modification of the Convolutional Neural Network (CNN) [7] incorporated additional gating mechanisms designed to extract sentiments associated with specific target aspects.…”

Section: Related Workmentioning

confidence: 99%

“…[20]: The solution proposed in this study utilized BiLSTM layers, syntax, and knowledge GCNNs to model the syntax and knowledge representations, respectively. [5]: This study's aspect-level sentiment analysis solution consisted of a modified CNN model with additional gating mechanisms. [19]: The solution proposed in this work utilized a BiLSTM layer as well as two GCNN layers, with the first GCNN capturing the emotional dependencies of target aspect terms and the second GCNN capturing the semantic dependencies of all words in the solution's input texts.…”

Section: Yadav Et Al (2021)mentioning

confidence: 99%

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Aspect-Level Sentiment Analysis through Aspect-Oriented Features

Busst,

Anbananthen,

Kannan

2024

HighTech. Innov. J.

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Aspect-level sentiment analysis is essential for businesses to comprehend sentiment polarities associated with various aspects within unstructured texts. Although several solutions have been proposed in recent studies in sentiment analysis, a few challenges persist. A significant challenge is the presence of multiple aspects within a single written text, each conveying its own sentiments. Besides this, the exploration of ensemble learning in the existing literature is limited. Therefore, this study proposes a novel aspect-level sentiment analysis solution that utilizes an ensemble of Bidirectional Long Short-Term Memory (BiLSTM) models. This innovative solution extracts aspects and sentiments and incorporates a rule-based algorithm to combine accurate sets of aspect and sentiment features. Experimental analysis demonstrates the effectiveness of the proposed methodology in accurately extracting aspect-level sentiment features from input texts. The proposed solution was able to obtain an F1 score of 92.98% on the SemEval-2014 Restaurant dataset when provided with the correct set of aspect-level sentiment features and an F1 score of 95.54% on the SemEval-2016 Laptop dataset when provided with the aspect-level sentiment features generated by the aspect-sentiment mapper algorithm. Doi: 10.28991/HIJ-2024-05-01-09 Full Text: PDF

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Section: Related Workmentioning

confidence: 99%

Section: Yadav Et Al (2021)mentioning

confidence: 99%

Aspect-Level Sentiment Analysis through Aspect-Oriented Features

Busst,

Anbananthen,

Kannan

2024

HighTech. Innov. J.

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“…For graph convolutional networks, whether the nodes are connected depends upon the syntactic mask matrix A mask . We updated the value of the ith node in layer l, as shown in Equation (3). The final output of the GCN is shown in Equation (4).…”

Section: Graph Convolutional Network Layermentioning

confidence: 99%

“…Sentiment analysis involves extracting viewpoints and judging emotional tendencies in subjective texts. Aspect-level sentiment analysis is more refined, particularly when judging the emotional polarity of texts in terms of given aspect words (e.g., positive, negative, and neutral) [1][2][3]. As shown in Figure 1, the sentence has two sides: "price" and "screen".…”

Section: Introductionmentioning

confidence: 99%

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

Gao,

Jiang,

Zou

et al. 2024

Applied Sciences

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Aspect-level sentiment analysis is a research focal point for natural language comprehension. An attention mechanism is a very important approach for aspect-level sentiment analysis, but it only fuses sentences from a semantic perspective and ignores grammatical information in the sentences. Graph convolutional networks (GCNs) are a better method for processing syntactic information; however, they still face problems in effectively combining semantic and syntactic information. This paper presents a sentiment-supported graph convolutional network (SSGCN). This SSGCN first obtains the semantic information of the text through aspect-aware attention and self-attention; then, a grammar mask matrix and a GCN are applied to preliminarily combine semantic information with grammatical information. Afterward, the processing of these information features is divided into three steps. To begin with, features related to the semantics and grammatical features of aspect words are extracted. The second step obtains the enhanced features of the semantic and grammatical information through sentiment support words. Finally, it concatenates the two features, thus enhancing the effectiveness of the attention mechanism formed from the combination of semantic and grammatical information. The experimental results show that compared with benchmark models, the SSGCN had an improved accuracy of 6.33–0.5%. In macro F1 evaluation, its improvement range was 11.68–0.5%.

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"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Islam,

Kabir,

Ghani

et al. 2024

Artif Intell Rev

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Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment.

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Research on non-dependent aspect-level sentiment analysis

Cited by 16 publications

References 9 publications

Aspect-Level Sentiment Analysis through Aspect-Oriented Features

Aspect-Level Sentiment Analysis through Aspect-Oriented Features

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

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