Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets (AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.     

基于ATT-DGRU的文本方面级别情感分析

方面级别情感分类是针对给定文本、分析其在给定方面所表达出的情感极性.现有的主流解决方案中,基于注意力机制的循环神经网络模型忽略了关键词邻近上下文信息的重要性,而结合卷积神经网络(Convolutional Neural Network,CNN)的多层模型不擅长捕捉句子级别的长距离依赖信息.因此,提出了一种基于截断循环神...     

Studies on waterline corrosion processes and corrosion product characteristics of carbon steel in 3.5 wt% NaCl solution

The waterline corrosion behaviors of carbon steel partially immersed in a 3.5 wt% NaCl solution were investigated using the wire beam electrode technique, and the effects of corrosion products on the processes of waterline corrosion were analyzed. The results demonstrated that the initial stage and development stage of waterline corrosion were mainly controlled by the concentration and diffusion of dissolved oxygen, respectively, and the deceleration stage of waterline corrosion was mainly affected by corrosion products. The main component of the yellow corrosion products was γ-FeOOH, and γ-FeOOH that exhibited a high reduction reactivity could be involved in the cathodic reaction. The black corrosion products were mainly composed of Fe₃O₄ with strong thermodynamic stability and the processes of dissolved oxygen diffusion and ion transports were obviously affected due to the continuous accumulation of Fe₃O₄ on the surface of the electrodes. Polarity reversals were observed on the single electrodes below the waterline, but the reasons for the phenomena were different from each other.     

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.     

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.     

基于汉语特征的中文对抗样本生成方法

深度神经网络容易受到来自对抗样本的攻击,例如在文本分类任务中修改原始文本中的少量字、词、标点符号即可改变模型分类结果.目前NLP领域对中文对抗样本的研究较少且未充分结合汉语的语言特征.从中文情感分类场景入手,结合了汉语象形、表音等语言特征,提出一种字词级别的高质量的对抗样本生成方法CWordCheater,涵盖字音、字形、标点符号等多个角度.针对形近字的替换方式,引入ConvAE网络完成汉字视觉向量的嵌入,进而生成形近字替换候选池.同时提出一种基于USE编码距离的语义约束方法避免对抗样本的语义偏移问题.构建一套多维度的对抗样本评估方法,从攻击效果和攻击代价两方面评估对抗样本的质量.实验结果表明,CWordAttacker在多个分类模型和多个数据集上能使分类准确率至少下降27.9%,同时拥有更小的基于视觉和语义的扰动代价.     

基于pu-learning的同行评议文本情感分析

最近几年逐渐出现了对同行评议文本情感分析的研究,包括通过同行评议文本预测审稿人的推荐状态的任务。现有模型融入了论文本身或摘要信息,采用神经网络学习论文或摘要的高层表示,结合同行评议文本预测审稿人的推荐状态,这使得模型变得非常复杂的同时结果却没有实质性的提高。为此,提出了OSA机制来提高情感分析模型中对观点句的关注度。具体来说,采用pu-learning从同行评议文本的前N个句子中学习观点句的特征,使每一个句子都得到一个观点句权重,将其应用于情感分析模型的倒数第二层,由此得到最终的预测结果。在ICLR2017—2018数据集上使用不同的情感分析模型对OSA进行了评估,实验结果验证了OSA的高效性,并在两个数据集上取得了优异的性能。     

基于高斯分布引导位置相关性权重的情感分类

针对当前情感分类方法通常忽略不同单词之间相对位置特征,导致模型难以学习到单词的最佳位置表示.为了解决这一问题,提出一种基于高斯分布引导位置相关性权重的情感分类算法.首先,计算每个单词与其他单词之间的位置相关性;其次,利用改进的高斯分布函数对位置相关性进行建模,并将其结果与单词的特征向量相乘,以生成单词的位置感知表示;最后,将算法集成到传统模型中以验证其有效性.实验结果表明,所提方法较传统模型获得了更高的准确率,在域内、域外和对抗评估指标上分别提高了2.98%、5.02%和10.55%,表明其具有较好的实用价值.     

一种基于改进支持向量机的文本倾向性分类算法

文本的倾向性分类器是文本倾向性分类的核心部分,它用于将待分类的文本映射到某一倾向性类别中去。传统支持向量机的核函数学习能力和泛化推广能力的平衡性有待提高,而且参数选择不易。对目前文本倾向性分类算法使用的传统的支持向量机进行了改进,一是构造了多核函数;二是使用粒子群算法对支持向量机的参数进行优化,平衡了核函数的全局性和局部性,更有利于对样本数据的学习和推广;最后利用改进的支持向量机构造文本倾向性分类算法。     

智能信息系统中手机产品评论的情感倾向分析

目前国内用户购买和使用大量不同手机产品。为帮助手机生产商识别用户评论的情感倾向、为其他潜在的手机用户提供手机产品购买建议,文中通过模块设计构建一个处理手机产品评论的智能信息系统,该系统用于挖掘和分析针对手机产品的评论信息。其中情感倾向分析是该系统的核心环节,因此文中研究并提出了一种基于条件随机场的针对手机产品的情感倾向识别方法,并通过采用多种实验手段寻找并验证该识别方法的有效性,从而完成对手机产品评论的高效、准确的自动识别。