Aspect-Level Opinion Mining of Online Customer Reviews

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram approach.Moreover, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining

Opinion summarization recapitulates the opinions about a common topic automatically.The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text.The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining.This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory(RNN-LSTM)deep learning model for short and exact synopsis using seq2seq model.It presents a paradigm shift with regard to the way extractive summaries are generated.Novel algorithms for word extraction using assertions are proposed.The semantic framework is well-grounded in this research facilitating the correct decision making process after reviewing huge amount of online reviews,considering all its important features into account.The advantages of the proposed solution provides greater computational efficiency,better inferences from social media,data understanding,robustness and handling sparse data.Experiments on the different datasets also outperforms the previous researches and the accuracy is claimed to achieve more than the baselines,showing the efficiency and the novelty in the research paper.The comparisons are done by calculating accuracy with different baselines using Rouge tool.

Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

At present,the immense development of social networks allows generating a significant amount of textual data,which has facilitated researchers to explore the field of opinion mining.In addition,the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality problem.This study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence(SI)-based algorithm,Harris hawks algorithm,to select the most relevant terms.The experimental study has been tested on two datasets:Arabic Jordanian General Tweets and Opinion Corpus for Arabic.In terms of accuracy and number of features,the results are better than those of other SI based algorithms,such as grey wolf optimizer and grasshopper optimization algorithm,and other algorithms in the literature,such as differential evolution,genetic algorithm,particle swarm optimization,basic and enhanced whale optimizer algorithm,slap swarm algorithm,and ant–lion optimizer.

Towards Mining Public Opinion: An Attention-Based Long Short Term Memory Network Using Transfer Learning

The Internet provides a large number of tools and resources, such as social media sites, online newsgroups, blogs, electronic forums, virtual communities, and online travel sites, for consumers to express their views or opinions regarding various issues. These opinions can help organizations like tourism to improve their products and services for their consumers. Opinion mining refers to a process of identifying emotions by applying Natural Language Processing (NLP) techniques to a pool of texts. This paper mainly focuses on mining public opinion from the hotel reviews domain. To do so, we proposed a novel technique called the Attention-Based Long Short Term Memory (Attention-LSTM) Network using a transfer learning approach. We empirically analyzed several machine learning and deep learning methods and observed our proposed technique provided an adequate performance for mining public opinion in the hotel reviews domain.

An Opinion Mining Task in Turkish Language： A Model for Assigning Opinions in Turkish Blogs to the Polarities

Global changes took place at a neck-breaking speed in lots of fields along with the Web 2.0 era, which can be stated as the new Internet trend. Web pages which once were a statical structure that can be said to become dynamic pages created by users, and in this regard they can be said to have been democratized by evolving. Social media, which were structured alongside with this era, by providing a large data flow for businesses, present new and improvable opportunities in the field of creating effective strategies. There are lots of blogs in today＇s Internet environment which includes customer ideas regarding the products/services that they possess. This environment, which in a way globalizes the customer ideas, is a new medium suitable for examination in terms of its increasing the business-customer interaction and due to its transporter nature; it provides the text data that may be analyzed in the field of Customer Relationship Management to businesses. Thus, businesses should follow blog environments to see how the product/service they provide is greeted in terms of the customer focus and it should be seen as an important job on which they can conduct effective analyses. For this purpose, a model proposal that will assign the ideas to the Turkish blogs was given in the study. Opinion mining methods were used in the model, and so to perceive a general look-on about products/services, a methodology was devised, which will assign the text based opinion data on the Turkish blogs to the poles. Success of the pole assignment of the model is evaluated with the precision measure.

负面评论引导的高维多元数据可视分析系统

随着互联网平台以及多用户社交网络的成熟,群体用户消费体验的参考价值日趋扩大,在海量评论数据中,负面评论对企业和消费者的参考价值更为突出,有效的面向负面评论的可视分析是有必要的.针对评论数据高维多元的特征,为了给企业和消费者提供全新的评论分析视角,以负面评论为切入点,给出负面评论的划定范围,提出了一个交互式的可视分析系统.首先,利用情感分析和意见挖掘方法处理用户评论数据,并提出评论个体影响力差异量化方法;其次设计了主题情感波纹图、评论比较视图等一系列交互式可视化表示方法,利用动态交互式方法构建多维度关联视图探索影响负面评论产生的因素,负面评论产生的原因及其个体化差异.3个案例的结果表明,所提系统是有效和实用的;同时,该系统还可扩展应用于其他领域的评论文本可视分析中.

基于在线品牌社区意见领袖的用户关键需求挖掘

随着社会的快速发展以及技术的不断进步,人们生活节奏不断加快,对产品的需求也在快速发生着变化。在线评论是目前用户需求表达的重要渠道。为克服不加区分挖掘所有评论的传统用户需求挖掘方法耗时过长,难以聚焦用户关键需求的问题,从用户关键性与需求关键性的双关键性角度出发,研发了一种基于在线品牌社区意见领袖的用户关键需求挖掘方法以快速获取用户重要需求,简称KEY-DEMANDS-OL。该方法依据帕累托法则,依托意见领袖评论大数据,采用优化的情感程度及初始改进率,结合KANO模型对用户关键需求进行挖掘。该方法不仅考虑了程度副词的语义信息,提高了情感分析的准确率,而且能够完成意见领袖的生成内容与KANO模型的自动整合,实现用户关键需求的获取与分类。研究结果表明,与挖掘所有评论的传统方法相比,KEY-DEMANDS-OL可以快速获取用户的关键需求,为企业制定产品优化方案提供辅助决策支持。

“一带一路”议题全球舆论话语图景与中国应对--基于2013-2023年全球社交媒体平台X的大数据研究

[研究目的]国际社交媒体反映全球民间舆论。通过分析X(原Twitter)平台上“一带一路”相关推文,了解国际舆论对该议题的态度和反应,以提出中国应对策略。[研究方法]基于数字媒体分析方法,对近十年的相关推文进行情感分析、主题分析和社会网络分析。[研究结论]发现X平台关于“一带一路”议题的舆情的三大现状,即;关注度高,受主要相关事件影响;情感波动显著,西方主流媒体叠加负面议题;中、英文推文场域相互溢出,中国主流媒体舆论引导力不足。基于此提出三个对策,即:强化舆情风险预测,提前制定应对方案;及时回应外部关切,强化沟通与危机管理;积极设置话题,强化舆论引导与议题塑造。

基于情感-主题协同演化模型的突发信息安全事件网络舆情分析

[目的/意义]针对突发信息安全事件,探索舆情生命周期中各阶段公众的情感倾向与关注的热点主题,快速挖掘网络舆情演化特征与发展趋势,有助于政府、企业和相关部门对舆情的监测与处理。[方法/过程]以滴滴事件为例,首先搜集事件相关微博评论文本,对舆情演化周期进行阶段划分,使用基于改进TF-IDF方法和LDA模型对各阶段进行主题挖掘,并构建融入领域情感词与表情符号的情感词典对各阶段下不同主题进行情感分析,得到舆情周期内主题与情感特征的协同演化趋势。[结果/结论]所提研究方法得到的舆情演化趋势能够有效反映突发事件各阶段的主题情感特征,有助于引导管控网络舆情,为舆情治理措施的制定提供科学依据。

基于演化聚类的网络舆情数据挖掘系统设计

为了提升网络舆情数据挖掘的召回率、精度和查准率,设计一个基于演化聚类的网络舆情数据挖掘系统。从综合需求、功能需求两方面进行深入分析,确定挖掘系统的实际要求。构建挖掘系统整体架构,采用垂直化结构分别设计支撑层、数据层、服务层、功能层,应用演化聚类建立舆情信息抓取关键词词库,计算随机演化初始向量,得到实验向量,分析不同维度的目标向量演化值,实现网络舆情抓取功能,确定适值函数实现信息挖掘。实验结果表明,设计系统的召回率能达到95%,精度能达到90%,且查准率接近99%,说明该系统的应用价值较高。

Lexicon and Deep Learning-Based Approaches in Sentiment Analysis on Short Texts

Social media is an essential component of our personal and professional lives. We use it extensively to share various things, including our opinions on daily topics and feelings about different subjects. This sharing of posts provides insights into someone’s current emotions. In artificial intelligence (AI) and deep learning (DL), researchers emphasize opinion mining and analysis of sentiment, particularly on social media platforms such as Twitter (currently known as X), which has a global user base. This research work revolves explicitly around a comparison between two popular approaches: Lexicon-based and Deep learning-based Approaches. To conduct this study, this study has used a Twitter dataset called sentiment140, which contains over 1.5 million data points. The primary focus was the Long Short-Term Memory (LSTM) deep learning sequence model. In the beginning, we used particular techniques to preprocess the data. The dataset is divided into training and test data. We evaluated the performance of our model using the test data. Simultaneously, we have applied the lexicon-based approach to the same test data and recorded the outputs. Finally, we compared the two approaches by creating confusion matrices based on their respective outputs. This allows us to assess their precision, recall, and F1-Score, enabling us to determine which approach yields better accuracy. This research achieved 98% model accuracy for deep learning algorithms and 95% model accuracy for the lexicon-based approach.

基于SnowNLP的微博网络舆情分析系统

随着微博、抖音、贴吧等新兴网络社交媒体的发展,大量用户开始喜欢使用这些平台进行发布和获取信息,因此累积了大量舆情数据。为了能够及时监测网络舆论动向,更好地维护互联网的安全运营和网络安全,针对实时微博数据,研究设计了一种基于SnowNLP的微博网络舆情分析系统。该系统由舆情数据采集、舆情数据分析和舆情数据可视化组成,能够实现微博数据文本挖掘、网络舆情数据情感分析、舆情数据与关键词匹配结果统计等功能,并能够对微博内容情感分析结果、用户等级、内容分词结果等进行可视化展示。实验测试结果表明:该系统功能运行正常,同时验证了设计方案的可行性和有效性。系统在网络舆情监测领域具有重要的应用价值。

网络舆情监测综合实验设计

网络自媒体海量舆论信息转发、情感两极分化、谣言混杂等极易引发网络舆情事件,对网络舆情监测综合能力的培养提出了更高的要求。针对现有教学中普遍缺乏综合实验这一实际情况,设计了一套网络舆情监测综合实验方案。首先,以成果导向教育理念为引导,确立了实验教学目标,规划了实验内容框架。然后,详细设计了从舆情文本获取到预处理,从主题挖掘到以基于变换器的双向编码器表征(bidirectional encoder representation from transformers, BERT)大模型为核心的情感分析、谣言检测等一系列实验模块。最后,使用网络舆情监测实例展示了实验效果。教学实践表明,实验方案能够使学生深入掌握网络舆情监测基本流程和实操技能。

基于情感特征和主题挖掘的日本福岛核污水排海事件舆情分析

福岛核污水排海事件是一个备受国内外公众关注的话题,探寻舆情演化过程、分析公众的情感倾向及其关注的焦点问题对舆情应对和公众沟通具有重要意义。本文采用文本挖掘技术,结合社交媒体、新闻媒体、公众评论等舆情数据,首先对排海事件舆情的演化趋势进行研究,并统计整理舆情发展的过程中公众参与讨论的话题;选择5个典型话题的评论文本研究公众的情感特征,并运用LDA主题分析模型挖掘公众关注的焦点问题。基于研究结果给出排海事件后续舆情应对及类似重大事件舆情应对的建议及对策,并为核能公众沟通在重大核事故应对问题上提供参考。

面向负面网络舆情的识别与追踪关键技术研究

当前网络负面舆情的追踪技术只完成了以关键词为基础的话题区分,对内部数据的相似性没有进一步研究,子话题的划分过程也过于简单。为了实现对网络负面舆情精准地识别和追踪,研究面向负面网络舆情的识别与追踪关键技术。首先,划分出负面网络舆情的子话题,分析数据对象的相似特性、完成聚类,获得一组子话题聚类的集合;其次,识别负面网络舆情事件,即从海量的事件信息中挖掘并识别出事件的本质,推断事件的因果关系,并对其发展趋势进行预测;最后,实现负面网络舆情话题的追踪。实验结果表明,此方法对负面网络舆情的识别检查的误检率平均值为1.1%,漏检率平均值为0.9%,通过实验结果能够得出此方法在负面网络舆情识别与追踪中具有较高的准确性和可靠性。