CE-HEAT: An Aspect-Level Sentiment Classification Approach With Collaborative Extraction Hierarchical Attention Network

Gao, Yang; Liu, Jianxun; Li, Pei; Zhou, Dong

doi:10.1109/access.2019.2954590

Cited by 20 publications

(8 citation statements)

References 14 publications

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“…One-unit extracts attribute and use them to capture specific information about the other layer. Experiments show that the proposed approach performed better than recent methods that only use aspect features [ 26 ]. Emotional vocabulary is an important source of thought extraction.…”

Section: Literature Of Opinion Miningmentioning

confidence: 99%

Deterministic solution of algebraic equations in sentiment analysis

Zahedi

Basiri

2023

Multimed Tools Appl

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Text mining methods usually use statistical information to solve text and language-independent procedures. Text mining methods such as polarity detection based on stochastic patterns and rules need many samples to train. On the other hand, deterministic and non-probabilistic methods are easy to solve and faster than other methods but are not efficient in NLP data. In this article, a fast and efficient deterministic method for solving the problems is proposed. In the proposed method firstly we transform text and labels into a set of equations. In the second step, a mathematical solution of ill-posed equations known as Tikhonov regularization was used as a deterministic and non-probabilistic way including additional assumptions, such as smoothness of solution to assign a weight that can reflect the semantic information of each sentimental word. We confirmed the efficiency of the proposed method in the SemEval-2013 competition, ESWC Database and Taboada database as three different cases. We observed improvement of our method over negative polarity due to our proposed mathematical step. Moreover, we demonstrated the effectiveness of our proposed method over the most common and traditional machine learning, stochastic and fuzzy methods.

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Section: Literature Of Opinion Miningmentioning

confidence: 99%

Deterministic solution of algebraic equations in sentiment analysis

Zahedi

Basiri

2023

Multimed Tools Appl

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“…The upgraded ALBERTC network was utilized to extract global phrase information and local emotion data while representing the initial aspect-level text as a word vector. Additionally, Gao et al (2019) have suggested the CE-HEAT approach to extract the uncommon sentiment terms. It has two hierarchical attention units, the first one collects sentiment characteristics from the sentiment attention layer.…”

Section: Literature Surveymentioning

confidence: 99%

Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization

Krosuri¹,

Aravapalli²

2023

PeerJ Computer Science

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Background In the modern era, Internet-based e-commerce world, consumers express their thoughts on the product or service through ranking and reviews. Sentiment analysis uncovers contextual inferences in user sentiment, assisting the commercial industry and end users in understanding the perception of the product or service. Variations in textual arrangement, complex logic, and sequence length are some of the challenges to accurately forecast the sentiment score of user reviews. Therefore, a novel improvised local search whale optimization improved long short-term memory (LSTM) for feature-level sentiment analysis of online product reviews is proposed in this study. Methods The proposed feature-level sentiment analysis method includes ‘data collection’, ‘pre-processing’, ‘feature extraction’, ‘feature selection’, and finally ‘sentiment classification’. First, the product reviews given from different customers are acquired, and then the retrieved data is pre-processed. These pre-processed data go through a feature extraction procedure using a modified inverse class frequency algorithm (LFMI) based on log term frequency. Then the feature is selected via levy flight-based mayfly optimization algorithm (LFMO). At last, the selected data is transformed to the improvised local search whale optimization boosted long short-term memory (ILW-LSTM) model, which categorizes the sentiment of the customer reviews as ‘positive’, ‘negative’, ‘very positive’, ‘very negative’, and ‘neutral’. The ‘Prompt Cloud dataset’ is used for the performance study of the suggested classifiers. Our suggested ILW-LSTM model is put to the test using standard performance evaluation. The primary metrics used to assess our suggested model are ‘accuracy’, ‘recall’, ’precision’, and ‘F1-score’. Results and Conclusion The proposed ILW-LSTM method provides an accuracy of 97%. In comparison to other leading algorithms, the outcome reveals that the ILW-LSTM model outperformed well in feature-level sentiment classification.

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“…Han et al [46] proposed a Multi-Attention Network (MAN) model which adopts several attention networks, the model solve the problem of the RNN-based model can't extract the potential correlation between relatively distant sentiment words and aspect words in complex statements, and the proposed model could achieve consistently superior results on three datasets. Gao et al [47] propose a collaborative extraction hierarchical attention network, this proposed method achieves better performance than the methods which only use aspect features to extract sentiment feature for aspect-level sentiment classification on SemEval competition data set. Yuan et al [48] proposed a sentiment analysis model based on multi-channel convolution and bidirectional GRU networks, and introduced an attention mechanism on the [50] proposed a bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model for sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

et al. 2020

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Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) have been widely used in the field of text sentiment analysis and have achieved good results. However, there is an anteroposterior dependency between texts, although CNN can extract local information between consecutive words of a sentence, it ignores the contextual semantic information between words. Bidirectional GRU can make up for the shortcomings that CNN can't extract contextual semantic information of long text, but it can't extract the local features of the text as well as CNN. Therefore, we propose a multi-channel model that combines the CNN and the bidirectional gated recurrent unit network with attention mechanism (MC-AttCNN-AttBiGRU). The model can pay attention to the words that are important to the sentiment polarity classification in the sentence through the attention mechanism and combine the advantages of CNN to extract local features of text and bidirectional GRU to extract contextual semantic information of long text, which improves the text feature extraction ability of the model. The experimental results on the IMDB dataset and Yelp 2015 dataset show that the proposed model can extract more rich text features than other baseline models, and can achieve better results than other baseline models. INDEX TERMS Convolutional Neural Network, Bidirectional gated recurrent unit network, attention mechanism, text sentiment orientation analysis.

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CE-HEAT: An Aspect-Level Sentiment Classification Approach With Collaborative Extraction Hierarchical Attention Network

Cited by 20 publications

References 14 publications

Deterministic solution of algebraic equations in sentiment analysis

Deterministic solution of algebraic equations in sentiment analysis

Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization

Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

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