Lexical attention and aspect-oriented graph convolutional networks for aspect-based sentiment analysis

2021 ◽  
pp. 1-12
Author(s):  
Wenwen Li ◽  
Shiqun Yin ◽  
Ting Pu
Keyword(s):  
Sentiment Analysis ◽  
Oriented Graph ◽  
Attention Mechanism ◽  
Experimental Results ◽  
The Other ◽  
Convolutional Networks ◽  
Other Hand ◽  
Syntactic Information ◽  
Benchmark Datasets ◽  
Syntactic Dependencies

 The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of different aspects in a text. In previous work, while attention has been paid to the use of Graph Convolutional Networks (GCN) to encode syntactic dependencies in order to exploit syntactic information, previous models have tended to confuse opinion words from different aspects due to the complexity of language and the diversity of aspects. On the other hand, the effect of word lexicality on aspects’ sentiment polarity judgments has not been considered in previous studies. In this paper, we propose lexical attention and aspect-oriented GCN to solve the above problems. First, we construct an aspect-oriented dependency-parsed tree by analyzing and pruning the dependency-parsed tree of the sentence, then use the lexical attention mechanism to focus on the features of the lexical properties that play a key role in determining the sentiment polarity, and finally extract the aspect-oriented lexical weighted features by a GCN.Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.

scholarly journals Attention-Enhanced Graph Convolutional Networks for Aspect-Based Sentiment Classification with Multi-Head Attention

2021 ◽  
Vol 11 (8) ◽  
pp. 3640
Author(s):  
Guangtao Xu ◽  
Peiyu Liu ◽  
Zhenfang Zhu ◽  
Jie Liu ◽  
Fuyong Xu
Keyword(s):  
Sentiment Classification ◽  
Experimental Results ◽  
Sentence Structure ◽  
Convolutional Network ◽  
Structure Information ◽  
Convolutional Networks ◽  
Dependency Tree ◽  
Syntactic Information ◽  
Benchmark Datasets ◽  
Reasonable Use

The purpose of aspect-based sentiment classification is to identify the sentiment polarity of each aspect in a sentence. Recently, due to the introduction of Graph Convolutional Networks (GCN), more and more studies have used sentence structure information to establish the connection between aspects and opinion words. However, the accuracy of these methods is limited by noise information and dependency tree parsing performance. To solve this problem, we proposed an attention-enhanced graph convolutional network (AEGCN) for aspect-based sentiment classification with multi-head attention (MHA). Our proposed method can better combine semantic and syntactic information by introducing MHA and GCN. We also added an attention mechanism to GCN to enhance its performance. In order to verify the effectiveness of our proposed method, we conducted a lot of experiments on five benchmark datasets. The experimental results show that our proposed method can make more reasonable use of semantic and syntactic information, and further improve the performance of GCN.

Natural Vulcanization Accelerators in Hevea Latex

1948 ◽  
Vol 21 (4) ◽  
pp. 853-859
Author(s):  
R. F. A. Altman
Keyword(s):  
Amino Acids ◽  
Experimental Results ◽  
The Other ◽  
Active Nitrogen ◽  
Nitrogen Bases ◽  
Other Hand ◽  
Decomposition Processes ◽  
Volatile Products ◽  
Nitrogenous Substances ◽  
The One

Abstract As numerous investigators have shown, some of the nonrubber components of Hevea latex have a decided accelerating action on the process of vulcanization. A survey of the literature on this subject points to the validity of certain general facts. 1. Among the nonrubber components of latex which have been investigated, certain nitrogenous bases appear to be most important for accelerating the rate of vulcanization. 2. These nitrogen bases apparently occur partly naturally in fresh latex, and partly as the result of putrefaction, heating, and other decomposition processes. 3. The nitrogen bases naturally present in fresh latex at later stages have been identified by Altman to be trigonelline, stachhydrine, betonicine, choline, methylamine, trimethylamine, and ammonia. These bases are markedly active in vulcanization, as will be seen in the section on experimental results. 4. The nitrogenous substances formed by the decomposition processes have only partly been identified, on the one hand as tetra- and pentamethylene diamine and some amino acids, on the other hand as alkaloids, proline, diamino acids, etc. 5. It has been generally accepted that these nitrogenous substances are derived from the proteins of the latex. 6. Decomposition appears to be connected with the formation of a considerable amount of acids. 7. The production of volatile nitrogen bases as a rule accompanies the decomposition processes. These volatile products have not been identified. 8. The active nitrogen bases, either already formed or derived from complex nitrogenous substances, seem to be soluble in water but only slightly soluble in acetone.

scholarly journals Comparison of the Fungicidal Properties of Different Humectants in a Tobacco Blend / Vergleich der fungistatischen Wirkung verschiedener Feuchthaltemittel bei einer ungesoßten Tabakmischung

1966 ◽  
Vol 3 (7) ◽  
Author(s):  
H. van Nooy
Keyword(s):  
Experimental Results ◽  
The Other ◽  
Other Hand ◽  
Tobacco Treatment

AbstractThe experimental results indicated in the present paper reveal that among all humectants admitted 1,3-butyleneglycol alone has marked fungicidal properties satisfying the requirements of practical tobacco treatment, and that, on the other hand, diethyleneglycol and glycerine practically do not have such qualities

scholarly journals Elastic Properties of Magnetorheological Elastomers in a Heterogeneous Uniaxial Magnetic Field

2018 ◽  
Vol 19 (10) ◽  
pp. 3045 ◽  
Author(s):  
Takehito Kikuchi ◽  
Yusuke Kobayashi ◽  
Mika Kawai ◽  
Tetsu Mitsumata
Keyword(s):  
Magnetic Field ◽  
Elastic Properties ◽  
Uniform Magnetic Field ◽  
Large Strain ◽  
Small Strain ◽  
Experimental Results ◽  
The Other ◽  
Magnetorheological Elastomers ◽  
Other Hand ◽  
Strain Model

Magnetorheological elastomers (MREs) are stimulus-responsive soft materials that consist of polymeric matrices and magnetic particles. In this study, large-strain response of MREs with 5 vol % of carbonyl iron (CI) particles is experimentally characterized for two different conditions: (1) shear deformation in a uniform magnetic field; and (2), compression in a heterogeneous uniaxial magnetic field. For condition (1), dynamic viscoelastic measurements were performed using a rheometer with a rotor disc and an electric magnet that generated a uniform magnetic field on disc-like material samples. For condition (2), on the other hand, three permanent magnets with different surface flux densities were used to generate a heterogeneous uniaxial magnetic field under cylindrical material samples. The experimental results were mathematically modeled, and the relationship between them was investigated. We also used finite-element method (FEM) software to estimate the uniaxial distributions of the magnetic field in the analyzed MREs for condition (2), and developed mathematical models to describe these phenomena. By using these practicable techniques, we established a simple macroscale model of the elastic properties of MREs under simple compression. We estimated the elastic properties of MREs in the small-strain regime (neo–Hookean model) and in the large-strain regime (Mooney–Rivlin model). The small-strain model explains the experimental results for strains under 5%. On the other hand, the large-strain model explains the experimental results for strains above 10%.

Effect of Manganese Addition on Strength and Fracture Toughness in Mg-6Al-1Zn Alloy

2006 ◽  
Vol 306-308 ◽  
pp. 857-862 ◽  
Author(s):  
Taisuke Sasaki ◽  
Tokuteru Uesugi ◽  
Yorinobu Takigawa ◽  
Kenji Higashi
Keyword(s):  
Fracture Toughness ◽  
Magnesium Alloy ◽  
Yield Strength ◽  
Ductile Fracture ◽  
Manganese Content ◽  
Experimental Results ◽  
The Other ◽  
Excessive Amount ◽  
Void Coalescence ◽  
Other Hand

The effect of manganese on strength and fracture toughness was investigated using five kinds of Mg-6Al-1Zn alloys. From the experimental results, the yield strength increased with increasing in manganese content until manganese content reached 0.14 wt. %. On the other hand, further increase in yield strength was not observed in case larger than 0.14 % of manganese was added. In addition, fracture toughness decreases with increasing manganese content. Fracture of magnesium alloy was ductile fracture by void coalescence. Adding excessive amount of manganese caused the increase in the presence of inclusions. This kind of particle easily became the nucleus of microvoid. As a conclusion, manganese should be added so that coarse manganese-bearing particle is not formed. Thus, 0.14 wt. % of manganese should be added to Mg-6Al-1Zn alloy in order to develop the alloy with well-balanced relationship between strength and fracture toughness.

Effects of Mandarin Cultivar on Quality of Mandarin Juice

2008 ◽  
Vol 14 (4) ◽  
pp. 307-313 ◽  
Author(s):  
F. Beltrán ◽  
A.J. Perez-López ◽  
J.M. López-Nicolás ◽  
A.A. Carbonell-Barrachina
Keyword(s):  
Vitamin C ◽  
Volatile Compounds ◽  
Experimental Results ◽  
The Other ◽  
Total Volatile ◽  
Color Coordinates ◽  
Juice Processing ◽  
Other Hand ◽  
Cielab Color

Eight mandarin cultivars have been analyzed for their content of vitamin C, minerals (Ca, Mg, K, Na, Fe, Cu, Mn, and Zn), CIELab color coordinates (L*, a*, b*, C*, and h ab), total volatile compounds content and sensory aroma intensity of juice. Experimental results proved that no important enough differences were found in the minerals contents to decide which mandarin cultivar was of higher quality. Clemenules provided the darkest juice with the highest vitamin C content and with the most intense mandarin aroma. On the other hand, Nova and Hernandina mandarin could be considered as the worst cultivars for juice production. Finally if Clemenules mandarins were not available for juice processing, Orogrande, Clemenpons, Ellendale, and Marisol could also be good options.

scholarly journals Modeling of Rate-Independent and Symmetric Hysteresis Based on Madelung’s Rules

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 352 ◽  
Author(s):  
Kairui Cao ◽  
Rui Li
Keyword(s):  
Phenomenological Models ◽  
Turning Points ◽  
Modeling Method ◽  
Experimental Results ◽  
The Other ◽  
Other Hand ◽  
The Relationship ◽  
With Memory

Hysteresis is a kind of nonlinearity with memory, which is usually unwanted in practice. Many phenomenological models have been proposed to describe the observed hysteresis. For instance, the Prandtl-Ishlinskii (PI) model, which consists of several backlash operators, is the most widely used. On the other hand, the well-known Madelung’s rules are always used to validate hysteresis models. It is worth pointing out that the PI model obeys Madelung’s rules. In this paper, instead of considering these rules as criteria, we propose a modeling method for symmetric hysteresis by directly constructing the trajectory based on Madelung’s rules. In the proposed method, turning points are recorded and wiped out according to the input value. After the implementation of the recording and wiping-out mechanisms, the curve which the current trajectory moves along can be determined and then the trajectory can be described. Furthermore, the relationship between the proposed method and the PI model is also investigated. The effectiveness of the presented method is validated by simulation and experimental results.

scholarly journals Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery

2020 ◽  
Author(s):  
Habiba H. Drias ◽  
Yassine Drias
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Association Rules ◽  
Great Majority ◽  
The Other ◽  
Frequent Patterns ◽  
Twitter Data ◽  
Other Hand ◽  
The World

A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.

scholarly journals Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

2019 ◽  
Author(s):  
Bowen Xing ◽  
Lejian Liao ◽  
Dandan Song ◽  
Jingang Wang ◽  
Fuzheng Zhang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Irrelevant Information ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Context Modeling ◽  
Fine Grained ◽  
Related Information ◽  
Modeling Process ◽  
Novel Variant

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.

scholarly journals A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification

2019 ◽  
Author(s):  
Yan Zhou ◽  
Longtao Huang ◽  
Tao Guo ◽  
Jizhong Han ◽  
Songlin Hu
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Joint Model ◽  
The State ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Global Information ◽  
Target Extraction ◽  
Benchmark Datasets ◽  
Tagging Methods

Target-Based Sentiment Analysis aims at extracting opinion targets and classifying the sentiment polarities expressed on each target. Recently, token based sequence tagging methods have been successfully applied to jointly solve the two tasks, which aims to predict a tag for each token. Since they do not treat a target containing several words as a whole, it might be difficult to make use of the global information to identify that opinion target, leading to incorrect extraction. Independently predicting the sentiment for each token may also lead to sentiment inconsistency for different words in an opinion target. In this paper, inspired by span-based methods in NLP, we propose a simple and effective joint model to conduct extraction and classification at span level rather than token level. Our model first emulates spans with one or more tokens and learns their representation based on the tokens inside. And then, a span-aware attention mechanism is designed to compute the sentiment information towards each span. Extensive experiments on three benchmark datasets show that our model consistently outperforms the state-of-the-art methods.

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