scholarly journals A Multi-Attention Network for Aspect-Level Sentiment Analysis

2019 ◽  
Vol 11 (7) ◽  
pp. 157 ◽  
Author(s):  
Qiuyue Zhang ◽  
Ran Lu
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Specific Aspect ◽  
Experimental Results ◽  
Sequence Information ◽  
Attention Network ◽  
Attention Networks ◽  
Recent Advances ◽  
Proposed Model ◽  
The Impact

Aspect-level sentiment analysis (ASA) aims at determining the sentiment polarity of specific aspect term with a given sentence. Recent advances in attention mechanisms suggest that attention models are useful in ASA tasks and can help identify focus words. Or combining attention mechanisms with neural networks are also common methods. However, according to the latest research, they often fail to extract text representations efficiently and to achieve interaction between aspect terms and contexts. In order to solve the complete task of ASA, this paper proposes a Multi-Attention Network (MAN) model which adopts several attention networks. This model not only preprocesses data by Bidirectional Encoder Representations from Transformers (BERT), but a number of measures have been taken. First, the MAN model utilizes the partial Transformer after transformation to obtain hidden sequence information. Second, because words in different location have different effects on aspect terms, we introduce location encoding to analyze the impact on distance from ASA tasks, then we obtain the influence of different words with aspect terms through the bidirectional attention network. From the experimental results of three datasets, we could find that the proposed model could achieve consistently superior results.

scholarly journals The impact of semantics on aspect level opinion mining

2021 ◽  
Vol 7 ◽  
pp. e558
Author(s):  
Eman M. Aboelela ◽  
Walaa Gad ◽  
Rasha Ismail
Keyword(s):  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Opinion Mining ◽  
Online Shopping ◽  
Experimental Results ◽  
Proposed Model ◽  
Online Comments ◽  
The Impact ◽  
F Measure ◽  
The Web

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products’ comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects’ synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands

2020 ◽  
Vol 6 (2) ◽  
pp. 771-782 ◽  
Author(s):  
Ozan Ozyegen ◽  
Sanaz Mohammadjafari ◽  
Emir Kavurmacioglu ◽  
John Maidens ◽  
Ayse Basar Bener
Keyword(s):  
Neural Networks ◽  
Mobile Radio ◽  
Experimental Results ◽  
Spectrum Prediction ◽  
The Impact ◽  
Land Mobile Radio

scholarly journals Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis

2019 ◽  
Vol 33 ◽  
pp. 371-378 ◽  
Author(s):  
Nan Xu ◽  
Wenji Mao ◽  
Guandan Chen
Keyword(s):  
Sentiment Analysis ◽  
Visual Information ◽  
Specific Aspect ◽  
Text And Image ◽  
Research Attention ◽  
Multimodal Data ◽  
Proposed Model ◽  
Memory Network ◽  
Multimodal Sentiment Analysis ◽  
The Given

As a fundamental task of sentiment analysis, aspect-level sentiment analysis aims to identify the sentiment polarity of a specific aspect in the context. Previous work on aspect-level sentiment analysis is text-based. With the prevalence of multimodal user-generated content (e.g. text and image) on the Internet, multimodal sentiment analysis has attracted increasing research attention in recent years. In the context of aspect-level sentiment analysis, multimodal data are often more important than text-only data, and have various correlations including impacts that aspect brings to text and image as well as the interactions associated with text and image. However, there has not been any related work carried out so far at the intersection of aspect-level and multimodal sentiment analysis. To fill this gap, we are among the first to put forward the new task, aspect based multimodal sentiment analysis, and propose a novel Multi-Interactive Memory Network (MIMN) model for this task. Our model includes two interactive memory networks to supervise the textual and visual information with the given aspect, and learns not only the interactive influences between cross-modality data but also the self influences in single-modality data. We provide a new publicly available multimodal aspect-level sentiment dataset to evaluate our model, and the experimental results demonstrate the effectiveness of our proposed model for this new task.

scholarly journals Blind Corner Propagation Model for IEEE 802.11p Communication in Network Simulators

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Sanchai Jaktheerangkoon ◽  
Kulit Na Nakorn ◽  
Kultida Rojviboonchai
Keyword(s):  
Variable Parameter ◽  
Propagation Model ◽  
Experimental Results ◽  
Ieee 802.11P ◽  
Connected Vehicle ◽  
Road Transportation ◽  
The Real ◽  
Proposed Model ◽  
Network Simulators ◽  
The Impact

Vehicular Ad Hoc Network (VANET) has been developed to enhance quality of road transportation. The development of safety applications could reduce number of road accidents. IEEE 802.11p is a promising standard for intervehicular communication, which would enable the connected-vehicle applications. However, in the well-known network simulators such as NS3 and Omnet, there is no propagation model that can simulate the IEEE 802.11p communication at blind corner realistically. Thus, in this paper, we conducted the real-world experiments of IEEE 802.11p in order to construct the model to describe the characteristics of the IEEE 802.11p communication at the blind corners. According to the experimental results, we observe that the minimum distance between the vehicle and the corner can effectively be represented as the key parameter in the model. Moreover, we have a variable parameter for adjusting the impact of the obstruction which could be different at each type of blind corners. The simulation results using our proposed model are compared with those using the existing obstacle model. The results showed that our proposed model is much more closely aligned with the real experimental results.

scholarly journals Aspect-Based Sentiment Analysis Using Aspect Map

2019 ◽  
Vol 9 (16) ◽  
pp. 3239
Author(s):  
Yunseok Noh ◽  
Seyoung Park ◽  
Seong-Bae Park
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
State Of The Art ◽  
Positional Information ◽  
Specific Aspect ◽  
Classification Model ◽  
Neural Models ◽  
Single Text ◽  
Proposed Model ◽  
Weakly Supervised

Aspect-based sentiment analysis (ABSA) is the task of classifying the sentiment of a specific aspect in a text. Because a single text usually has multiple aspects which are expressed independently, ABSA is a crucial task for in-depth opinion mining. A key point of solving ABSA is to align sentiment expressions with their proper target aspect in a text. Thus, many recent neural models have applied attention mechanisms to learning the alignment. However, it is problematic to depend solely on attention mechanisms to achieve this, because most sentiment expressions such as “nice” and “bad” are too general to be aligned with a proper aspect even through an attention mechanism. To solve this problem, this paper proposes a novel convolutional neural network (CNN)-based aspect-level sentiment classification model, which consists of two CNNs. Because sentiment expressions relevant to an aspect usually appear near the aspect expressions of the aspect, the proposed model first finds the aspect expressions for a given aspect and then focuses on the sentiment expressions around the aspect expressions to determine the final sentiment of an aspect. Thus, the first CNN extracts the positional information of aspect expressions for a target aspect and expresses the information as an aspect map. Even if there exist no data with annotations on direct relation between aspects and their expressions, the aspect map can be obtained effectively by learning it in a weakly supervised manner. Then, the second CNN classifies the sentiment of the target aspect in a text using the aspect map. The proposed model is evaluated on SemEval 2016 Task 5 dataset and is compared with several baseline models. According to the experimental results, the proposed model does not only outperform the baseline models but also shows state-of-the-art performance for the dataset.

scholarly journals Biomedical document triage using a hierarchical attention-based capsule network

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Jian Wang ◽  
Mengying Li ◽  
Qishuai Diao ◽  
Hongfei Lin ◽  
Zhihao Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Information Extraction ◽  
Precision Medicine ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Experimental Results ◽  
Biomedical Domain ◽  
Proposed Model ◽  
Document Triage

Abstract Background Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences. Results In this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora. Conclusions Experimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.

A multi-grained aspect vector learning model for unsupervised aspect identification

2021 ◽  
pp. 1-11
Author(s):  
Jinglei Shi ◽  
Junjun Guo ◽  
Zhengtao Yu ◽  
Yan Xiang
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Topic Models ◽  
Experimental Results ◽  
Coarse Grained ◽  
Product Reviews ◽  
Fine Grained ◽  
Proposed Model ◽  
Identification Model

Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms.

scholarly journals Capacity Visual Attention Networks

10.29007/lcmk ◽  
2018 ◽  
Author(s):  
Marcus Edel ◽  
Joscha Lausch
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Machine Translation ◽  
Input Data ◽  
Counting Process ◽  
Model Output ◽  
Attention Networks ◽  
Attention Model ◽  
Proposed Model ◽  
Extract Information

Inspired by recent work in machine translation and object detection, we introduce an attention-based model that automatically learns to extract information from an image by adaptively assigning its capacity across different portions of the input data and only processing the selected regions of different sizes at high resolution. This is achieved by combining two modules: an attention sub-network which uses a mechanism to model a human-like counting process and a capacity sub-network. This sub-network efficiently identifies input regions for which the attention model output is most sensitive and to which we should devote more capacity and dynamically adapt the size of the region. We focus our evaluation on the Cluttered MNIST, SVHN, and Cluttered GTSRB image datasets. Our findings indicate that the proposed model is able to drastically reduce the number of computations, compared with traditional convolutional neural networks, while maintaining similar or better performance.

scholarly journals Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5276 ◽  
Author(s):  
Mohammed Abbas Mohammed Almansor ◽  
Chongfu Zhang ◽  
Wasiq Khan ◽  
Abir Hussain ◽  
Naji Alhusaini
Keyword(s):  
Sentiment Analysis ◽  
Feature Weighting ◽  
Instance Selection ◽  
Data Set ◽  
Bee Colony ◽  
Proposed Model ◽  
Cross Lingual ◽  
Translation Errors ◽  
Language Divergence ◽  
The Impact

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models.

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