A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency parsersare limited by language and grammatical constraints. Therefore, in this work, asequential pattern-based rule mining model, which does not have such constraints,is proposed for cross-domain opinion target extraction from product reviews inunknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also revealsthe difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the firststage, the aspects of reviews are extracted from the target domain using the rulesautomatically generated from source domains. The aspects are also transferredfrom the source domains to a target domain. Moreover, aspect pruning is appliedto further improve the performance of aspect extraction. In the second stage, theopinion target is extracted among the aspects extracted at the former stage usingthe rules automatically generated for opinion target extraction. The proposedmodel was evaluated on several benchmark datasets in different domains andcompared against the literature. The experimental results revealed that the opiniontargets of the reviews in unknown domains can be extracted with higher accuracythan those of the previous works.

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.

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.

A Novel Feature-based Method for Sentiment Analysis of Chinese Product Reviews

Sentiment analysis of online reviews and other user generated content is an important research problem for its wide range of applications.In this paper,we propose a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews.Specifically,an opinionated document is modeled by a set of feature-based vectors and corresponding weights.Different from previous work,our model considers modifying relationships between words and contains rich sentiment strength descriptions which are represented by adverbs of degree and punctuations.Dependency parsing is applied to construct the feature vectors.A novel feature weighting algorithm is proposed for supervised sentiment classification based on rich sentiment strength related information.The experimental results demonstrate the effectiveness of the proposed method compared with a state of the art method using term level weighting algorithms.

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.

Enhancement of Sentiment Analysis Using Clause and Discourse Connectives

The sentiment of a text depends on the clausal structure of the sentence and the connectives’discourse arguments.In this work,the clause boundary,discourse argument,and syntactic and semantic information of the sentence are used to assign the text’s sentiment.The clause boundaries identify the span of the text,and the discourse connectives identify the arguments.Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence,a deeper-level semantic analysis is required for the correct analysis of sentiments.Hence,in this study,explicit connectives in Malayalam are considered to identify the discourse arguments.A supervised method,conditional random fields,is used to identify the clause boundary and discourse arguments.For the study,1,000 sentiment sentences from Malayalam documents were analyzed.Experimental results show that the discourse structure integration considerably improves sentiment analysis performance from the baseline system.

Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets

The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision making that can improve quality, profitability, productivity, competitiveness and customer satisfaction. Sentiment analysis is the field that is concerned with the classification and analysis of user generated text under defined polarities. Despite the upsurge of research in sentiment analysis in recent years, there is a dearth in literature on sentiment analysis applied to banks social media data and mostly on African datasets. Against this background, this study applied machine learning technique (support vector machine) for sentiment analysis of Nigerian banks twitter data within a 2-year period, from 1st January 2017 to 31st December 2018. After crawling and preprocessing of the data, LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data. The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks. The results show that the accuracy of the classifier was 71.8367%. The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures. Applying the model in predicting the sentiments of the five Nigerian banks twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets. The scatter plots for the sentiments series indicated that, majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.

“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。

基于分布式爬虫的微博舆情监督与情感分析系统设计

互联网的兴起使微博等自媒体平台成为网民表达意见的主要途径。同时,网络舆情的迅速传播使得网民舆论管理成为一个难题。针对传统方法在微博舆情管理上的局限性,文章设计一种基于分布式爬虫的微博舆情监测与情感分析系统,并借助情感分析和LDA主题提取技术,对热点事件进行分析,帮助政府和企业更好地把握舆情发展动态,捍卫其社会公信力。

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。

基于情感分析的高校舆情预测系统

为促进高校管理效能的全面提升,构建和谐校园、智慧校园,设计了基于文本情感分析技术的高校舆情预测系统。系统通过对比情感字典,提取留言中的情感特征数据和主题特征数据,融合时间数据和热度数据,建立情感特征模型,使用损失函数修正模型,支持向量机预测舆情爆发的可能性。

Investigating User Ridership Sentiments for Bike Sharing Programs

Bike sharing is considered a state-of-the-art transportation program. It is ideal for short or medium trips providing riders the ability to pick up a bike at any self-serve bike station and return it to any bike station located within the system’s coverage area. The bike sharing programs in the United States are still very young compared to those in European countries. Washington DC was the first jurisdiction to devise a third generation bike sharing system in the US in 2008. To evaluate the popularity of a bike sharing program, a sentiment analysis of the riders’ feedback can be performed. Twitter is a great platform to understand people’s views instantly. Social media mining is, thus, gaining popularity in many research areas including transportation. Social media mining has two major advantages over conventional attitudinal survey methods—it can easily reach a large audience and it can reflect the true behavior of participants because of the anonymity social media provides. It is known that self-imposed censor is common in responding to conversational attitudinal surveys. This study performed text mining on the tweets related to a case study (Capital Bike share of Washington DC) to perform sentiment analysis or opinion mining. The results of the text mining mostly revealed higher positive sentiments towards the current system.

MELex: The Construction of Malay-English Sentiment Lexicon

Currently,the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon.Thus,this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis.In this study,a new lexicon for sentiment analysis is constructed.A detailed review of existing approaches has been conducted,and a new bilingual sentiment lexicon known as MELex(Malay-English Lexicon)has been generated.Constructing MELex involves three activities:seed words selection,polarity assignment,and synonym expansions.Our approach differs from previous works in that MELex can analyze text for the two most widely used languages in Malaysia,Malay,and English,with the accuracy achieved,is 90%.It is evaluated based on the experimentation and case study approaches where the affordable housing projects in Malaysia are selected as case projects.This finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian context.The novel aspects of this paper are two-fold.Firstly,it introduces the new technique in assigning the polarity score,and second,it improves the performance over the classification of mixed language content.

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

“If We Only Knew How You Feel”—A Comparative Study of Automated vs. Manual Classification of Opinions of Customers on Digital Media

The Web development has drastically changed the human interaction and communication, leading to an exponential growth of data generated by users in various digital media. This mass of data provides opportunities for understanding people’s opinions about products, services, processes, events, political movements, and organizational strategies. In this context, it becomes important for companies to be able to assess customer satisfaction about their products or services. One of the ways to evaluate customer sentiment is the use of Sentiment Analysis, also known as Opinion Mining. This research aims to compare the efficiency of an automatic classifier based on dictionary with the classification by human jurors in a set of comments made by customers in Portuguese language. The data consist of opinions of service users of one of the largest Brazilian online employment agencies. The performance evaluation of the classification models was done using Kappa index and a Confusion Matrix. As the main finding, it is noteworthy that the agreement between the classifier and the human jurors came to moderate, with better performance for the dictionary-based classifier. This result was considered satisfactory, considering that the Sentiment Analysis in Portuguese language is a complex task and demands more research and development.

一般性与反转类突发事件网络舆情主题及情感演化对比研究

[目的/意义]结合主题挖掘与情感分析,旨在对比一般性突发事件与反转类突发事件的舆情发展变化,揭示突发事件网络舆情演化规律,描绘舆情主题在生命周期不同阶段的特点,勾勒出网民的情感演变与讨论主题间密切关系,为政府及相关部门针对不同类型事件监管舆论、引导公众情绪提供决策支持。[方法/过程]引入生命周期理论、危机传播理论、时间序列方法,合理划分不同类型事件的舆情演化周期,在此基础上利用TFIDF+Word2Vec+K-Means处理文本,提取有效主题特征词,构建情感词典进行情感倾向性分析。[结果/结论]研究结果表明,舆情反转突发事件比一般性突发事件演化更复杂、主题数量与内容更丰富、负面情绪持续时间更久。对网络舆情引导提出针对性的建议:把握共同特征,抓住关键角色;加强舆情动态监测,做好舆情危机预警;提高政府办事能力,积极应对舆情反转。

KENAOTE:一种知识增强的方面和意见对提取多任务学习模型

方面和意见对提取旨在根据给定句子提取方面和意见项并匹配关系,然而相关研究通常独立提取方面和意见项,而不识别关系。为了识别方面和意见项关系,提出一种知识增强的方面和意见对提取多任务学习模型。首先使用预训练语言模型为文本生成具有语义信息的词向量,为了实现知识增强的效果,使用遮蔽注意力的方式将知识图谱的语义信息融入词向量中,然后使用基于距离注意力和条件随机场的序列标注方法提取方面和意见项,最后再将提取的方面和意见项两两匹配预测对应关系。为了加强方面和意见项提取模块和匹配模块的联系,采用共享编码层的方式实现联合训练。在训练流程中,匹配模块采用真实标签作为输入,在测试过程中采用提取模块的结果作为输入。为了证明模型的有效性,使用三个通用领域数据集进行对比实验,该模型在方面和意见项匹配任务中F 1值分别达到66.99%、75.17%和67.30%,并优于其他比较模型。

基于NLP定制模型的游客感知研究——以重庆市鹅岭公园为例

游客感知对于了解人民需求、提升城市建设质量有着重要意义。以公园网络文本为数据训练NLP定制模型,更适用于风景园林领域的需求,使公园治理与设计更加智能与高效。基于深度学习平台,训练三个多标签文本分类、情感倾向分析、评论观点提取三个模型处理公园网络文本,从“时间—评价对象—评价对象下的感知要素”多层次分析公园游客情感倾向特征,挖掘重点感知要素。研究表明:(1)鹅岭公园游客感知整体积极性较高,6类评价对象中园外景观受关注度最高,自然景观与游客积极情绪成正比,设施配套消极情绪最高,停车位是亟需解决的问题。(2)在60个高频感知要素中,7个要素与游客积极情绪概率成显著正比,其中5个正相关,2个负相关。(3)采取“文本分类—高频词提取—情感分析”的分析顺序,可以挖掘词频低但有重要影响的感知要素。(4)NLP定制模型提供的属性级情感分析可以减少情感分析误差,使研究更准确。研究鹅岭公园游客情感与公园重点感知要素,为鹅岭公园的建设提升提出优化建议,为自然语言处理在风景园林中的应用提供了参考。

基于情感-主题-讽刺混合模型的讽刺检测研究

讽刺检测是观点挖掘的一个子任务,主要目的是识别用户在书面文本中表达的观点或情感。文本中讽刺句往往具有混合的情感极性,正确识别讽刺句和非讽刺句在情感分析中起着至关重要的作用。讽刺检测方法一般都采用机器学习分类器,其中分类器的训练主要基于简单的词汇或基于词典的特征。本研究的目的是建立一个无监督的概率关系模型,根据微博中词语的情感分布来识别讽刺主题。模型基于主题级分布估计相关情感,评估出现在短文本中的情感相关词,给出情感相关标签。实验结果表明,该模型在讽刺检测方面优于其他最新的基线模型,非常适合于短文本的讽刺预测。