Aspect Based Sentiment Analysis With Feature Enhanced Attention CNN-BiLSTM

Meng, Wei; Wei, Yongqing; Liu, Peiyu; Zhu, Zhenfang; Yin, Hongpeng

doi:10.1109/access.2019.2952888

Cited by 65 publications

(33 citation statements)

References 31 publications

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“…Based on the Keras deep learning framework, a BiLSTM-Attention model is constructed for defect texts classification. Considering the experience of model super parameter setting in [36,37], based on the grid search method [38][39][40], the parameters of the model are optimized. The model's optimal parameter settings are obtained as shown in Table 6.…”

Section: Case Study and Analysismentioning

confidence: 99%

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

et al. 2020

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In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

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Section: Case Study and Analysismentioning

confidence: 99%

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

et al. 2020

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“…Meng et al [9] adopted a CNN to extract high-level feature representation from improved word embedding layer. Bi-LSTM is used to capture local and global semantic information after that an attention layer is employed to highlight relevant aspect term features.…”

Section: Related Workmentioning

confidence: 99%

“…Because of the efficient performance of BERT [10] in feature representation compared to conventional syntax embeddings such as Glove [12] or word2vec [9] this research adopts and fine-tunes the BERT model for the AOM task. As the first step of the FGAOM model, the language BERT model is finetuned by pre-training it using three domain-specific corpora (see, Table I).…”

Section: A Domain Adaption and Embedding Layermentioning

confidence: 99%

“…Thus, we adapt the given local and global context to match the data processing method of BERT. Since previous studies [9] separately process aspect terms and context sequences and then model their interactions, the FGAOM adopts Semantic-Relative Distance (SRD) [21] to calculate if a semantic feature lies within local context interval of specific aspect term-based on the intuition that most significant context information belongs to aspect local context. Predominantly, the number of tokens between aspect term and semantic token considered as SRD of aspect-semantic pair (see, equation 3).…”

Section: A Domain Adaption and Embedding Layermentioning

confidence: 99%

“…Differently from computer vision where low-level feature extraction can effectively be accomplished with transfer learning, most of NLP tasks require training from scratch for every application with an exception for word embedding models [9] (e.g., word2vec, Glove, Fast Text, etc.) that is trained on huge wide-scaled unlabeled text corpora, however they only deliberate syntactic information and suffer from semantic independence since the same word have the same embedding despite the diversity in context.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining

et al. 2020

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Despite the great manufactures' efforts to achieve customer satisfaction and improve their performance, social media opinion mining is still on the fly a big challenge. Current opinion mining requires sophisticated feature engineering and syntactic word embedding without considering semantic interaction between aspect term and opinionated features, which degrade the performance of most of opinion mining tasks, especially those that are designed for smart manufacturing. Research on intelligent aspect level opinion mining (AOM) follows the fast proliferation of user-generated data through social media for industrial manufacturing purposes. Google's pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT) widely overcomes existing methods in eleven natural language processing (NLP) tasks, which makes it the standard way for semantic text representation. In this paper, we introduce a novel deep learning model for fine-grained aspect-based opinion mining, named as FGAOM. First, we train the BERT model on three specific domain corpora for domain adaption, then use adjusted BERT as embedding layer for concurrent extraction of local and global context features. Then, we propose Multi-head Self-Attention (MSHA) to effectively fuse internal semantic text representation and take advantage of convolutional layers to model aspect term interaction with surrounding sentiment features. Finally, the performance of the proposed model is evaluated via extensive experiments on three public datasets. Results show that performance of the proposed model outperforms performances of recent the-of-the-art models.

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A novel hybrid deep learning model for aspect based sentiment analysis

Kuppusamy

Selvaraj²

2022

Concurrency and Computation

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Summary The usage of social media, forums, and e‐commerce websites have been widely increased. Feedback from customers has a big impact on the final product. A service provider, merchant, or manufacturer need all the information, even if it is just a comment or a review about a service or a product. So, it is vital to look at input from users, and therefore sentiment analysis has received a lot of interest. Sentiment analysis is a method for identifying and analyzing text in order to determine the features, qualities, and viewpoints of particular user. Extracting user aspects is the main part of this process, and it is used to group the user aspects. In recent years, convolutional neural network (CNN) models have gained popularity in natural language processing. Thus, this research proposes a novel hybrid CNN model by concatenating the bidirectional long short‐term memory and CNN models to process the data sequentially by learning their high‐level features. The concatenated method minimizes the loss of critical information. Benchmark product reviews and hotel review datasets are employed in the experiments, and accuracies of 93.6% for the product review dataset and 92.7% for the hotel review dataset are achieved by the proposed hybrid model when compared to state‐of‐the‐art techniques.

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Aspect Based Sentiment Analysis With Feature Enhanced Attention CNN-BiLSTM

Cited by 65 publications

References 31 publications

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining

A novel hybrid deep learning model for aspect based sentiment analysis

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