Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis

Pham, Duc-Thinh; Le, Anh-Cuong

doi:10.1016/j.ijar.2018.08.003

Cited by 71 publications

(36 citation statements)

References 4 publications

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“…Some of the information (gender, history of hypertension, family members with histories of hypertension, etc.) has two or more values, so it needs to be processed by one-hot encoding [ 23 ]. For example, in Table 4 , index values from 7 to 9 indicate race types (African American, White, and Asian).…”

Section: Methodsmentioning

confidence: 99%

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Liu

Yin

Yang

2020

Sensors

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As is known, cerebral stroke has become one of the main diseases endangering people’s health; ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the incidence rate of the disease. However, it is difficult to predict the ischaemic stroke because the data related to the disease are multi-modal. To achieve high accuracy of prediction and combine the stroke risk predictors obtained by previous researchers, a method for predicting the probability of stroke occurrence based on a multi-model fusion convolutional neural network structure is proposed. In such a way, the accuracy of ischaemic stroke prediction is improved by processing multi-modal data through multiple end-to-end neural networks. In this method, the feature extraction of structured data (age, gender, history of hypertension, etc.) and streaming data (heart rate, blood pressure, etc.) based on a convolutional neural network is first realized. A neural network model for feature fusion is then constructed to realize the feature fusion of structured data and streaming data. Finally, a predictive model for predicting the probability of stroke is obtained by training. As shown in the experimental results, the accuracy of ischaemic stroke prediction reached 98.53%. Such a high prediction accuracy will be helpful for preventing the occurrence of stroke.

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Section: Methodsmentioning

confidence: 99%

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Liu

Yin

Yang

2020

Sensors

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“…We compare the Hybrid ELMo and Wikipedia methods that we propose for the determination of aspect term vector and aspect keyword vector with the method of determining the aspect term vector and aspect keyword vector from previous research using Glove [4,5,29], Word2vec [29] and Fasttext [8]. In addition, we also compare with ELMo without modification.…”

Section: Comparisonmentioning

confidence: 99%

“…Glove [4,5,29] Word2vec [29] Fasttext [8] ELMo positive in the aspect of Price fairness. It can be seen in Fig.…”

Section: Metode Atementioning

confidence: 99%

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan¹,

Sarno²,

Sungkono³

2019

IJIES

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Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.

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“…Word embeddings are being used for word representations. More semantics can be captured by using Word2Vec, Glove embeddings, and one-hot character vectors (Pham & Le, 2018).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Guo

Zhang

2020

Sage Open

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By using machine learning technique, this article presents sentiment and concept analyses on 48,043 articles published in The Economist from 1991 through 2016. The Economist is one of the world’s most influential political and economic magazines. The article analyzes and compares the magazine’s sentiment orientations toward the Group of Seven’s ingroup member countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and its outgroup member country China. The sentiment analyses are performed on and compared between different periods of Clinton’s, Bush’s, and Obama’s administrations in the United States; Major’s, Blair’s, Brown’s, and Cameron’s cabinets in the United Kingdom; and Kohl’s, Schröder’s, and Merkel’s in Germany. The relationship between China hosting the Olympic Games or its growing economic power and the magazine’s sentiment orientations toward the country is examined. The concept analysis on the articles with extreme positivity or negativity shows that there is no difference between the ingroup and outgroup members in the topics covered in The Economist.

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Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis

Cited by 71 publications

References 4 publications

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

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