Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm

Feng, Jinzhan; Cai, Shuqin; Ma, Xiaofeng

doi:10.1007/s10586-017-1626-5

Cited by 35 publications

(15 citation statements)

References 31 publications

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“…Rectified linear units (ReLUs) were used for the attribution of the non-linear properties of the decision mapping function [ 22 ]. Batch normalization (BN) was used before each full connection layer and after each convolution module to improve learning speed and reduce initialization requirements [ 23 ]. The Softmax function was used to process the full-connectivity data of the last layer by highlighting the maximum value and limiting the eigenvalues of other nerve units below the maximum value.…”

Section: Methodsmentioning

confidence: 99%

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Nie

Lin

et al. 2021

IJMS

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Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L−1) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.

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Section: Methodsmentioning

confidence: 99%

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Nie

Lin

et al. 2021

IJMS

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“…The concatenated word embeddings are fed into the models (Do et al,, Prasad, Maag, and Alsadoon, 2019 [9]). It has been proved that the use of POS tagging as input can improve the performance of aspect extraction, with gains from 1% [18,20] to 4% [24]. Apart from the POS, concepts that are closely related to the affections are suggested to be added as word embeddings [25,26].…”

Section: Input Vectorsmentioning

confidence: 99%

“…The update rules for parameter set using Adam and AdaBelief are given by Eqs. ( 23) and (24), respectively:…”

Section: Loss ¼àmentioning

confidence: 99%

Tourist Sentiment Mining Based on Deep Learning

Li¹,

Yang²,

Du³

2022

Artificial Intelligence

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Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

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“…Co-occurrence In recognition of the fact that it is unrealistic to read all the online reviews, let alone talk of comprehending their meaning within a short period of time, hence the need for the ABSA. Feng, et al [104] used Co-occurrence technique for implicit oriented aspect identification in English and Chinese languages, by considering the topic and the matching degree of aspects, sentiment words, as well as human language habit of being the major aspect factors. However, the limitation of the study is that it could not cover diverse domains as such limited to only mobile phone reviews.…”

Section: ) Implicit Aspect Extraction Techniques With Their Associatmentioning

confidence: 99%

“…However, the approach suffered low recall value constrain in all the domains, which entails missing some valid aspect terms. [104] used CNN to develop a novel approach for identifying implicit aspect by taking the two key factors of the aspects as a topic and the match degree of sentiment and aspects words, as well as the human language habit. The limitation of the approach observed in the study is the fact that it mainly focused on mobile phone reviews which resulted in the inability to correctly identify significant amount of aspect or sentiment words.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

et al. 2020

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Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain. INDEX TERMS aspect-based sentiment analysis, aspect extraction, explicit aspect, feature extraction, implicit aspect.

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Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm

Cited by 35 publications

References 31 publications

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Tourist Sentiment Mining Based on Deep Learning

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

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