LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social media.However,limited research has been conducted in this domain using theLexDeep approach.This study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 pandemic.First,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)thepandemic.Subsequently,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the tweets.The performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and analysis.The resultsdemonstrated that all LexDeep models with simple embedding outperformedthe SVM.This confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific sentiments.In terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe affected.Future research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.

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.

Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social media.In literature,it has been reported that SA data is created for English language in excess of any other language.It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language.An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.Neural word embedding has been employed in literature,since it is less labor-intensive than automatic feature engineering.By ignoring the context of sentiment,most of the word-embedding models follow syntactic data of words.The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets(DFODLSAAT)model.The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language.At first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful format.In addition,TF-IDF model is exploited as a feature extractor to generate the feature vectors.Besides,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of sentiments.At last,the hyperparameters of ABLSTM model are optimized using DFO algorithm.The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects.The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.

Sentiment Lexicon Construction Based on Improved Left-Right Entropy Algorithm

A novel method of constructing sentiment lexicon of new words(SLNW)is proposed to realize effective Weibo sentiment analysis by integrating existing lexicons of sentiments,lexicons of degree,negation and network.Based on left-right entropy and mutual information(MI)neologism discovery algorithms,this new algorithm divides N-gram to obtain strings dynamically instead of relying on fixed sliding window when using Trie as data structure.The sentiment-oriented point mutual information(SO-PMI)algorithm with Laplacian smoothing is used to distinguish sentiment tendency of new words found in the data set to form SLNW by putting new words to basic sentiment lexicon.Experiments show that the sentiment analysis based on SLNW performs better than others.Precision,recall and F-measure are improved in both topic and non-topic Weibo data sets.

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

Applying English Idiomatic Expressions to Classify Deep Sentiments in COVID-19 Tweets

Millions of people are connecting and exchanging information on social media platforms,where interpersonal interactions are constantly being shared.However,due to inaccurate or misleading information about the COVID-19 pandemic,social media platforms became the scene of tense debates between believers and doubters.Healthcare professionals and public health agencies also use social media to inform the public about COVID-19 news and updates.However,they occasionally have trouble managing massive pandemic-related rumors and frauds.One reason is that people share and engage,regardless of the information source,by assuming the content is unquestionably true.On Twitter,users use words and phrases literally to convey their views or opinion.However,other users choose to utilize idioms or proverbs that are implicit and indirect to make a stronger impression on the audience or perhaps to catch their attention.Idioms and proverbs are figurative expressions with a thematically coherent totality that cannot understand literally.Despite more than 10%of tweets containing idioms or slang,most sentiment analysis research focuses on the accuracy enhancement of various classification algorithms.However,little attention would decipher the hidden sentiments of the expressed idioms in tweets.This paper proposes a novel data expansion strategy for categorizing tweets concerning COVID-19.The following are the benefits of the suggested method:1)no transformer fine-tuning is necessary,2)the technique solves the fundamental challenge of the manual data labeling process by automating the construction and annotation of the sentiment lexicon,3)the method minimizes the error rate in annotating the lexicon,and drastically improves the tweet sentiment classification’s accuracy performance.

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects.Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy.The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms.Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process.In unsupervised mechanisms,a lexicon is constructed for storing polarity terms.The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms.In addition,most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process.Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process.This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research.A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms.An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms.An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically.The experimental results are conducted on two datasets to measure the over-all accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

融合情感词典与深度学习的文本情感分析研究

文本情感分析是自然语言处理领域内的重点研究方向。当前Word2vec词向量结合神经网络的深度学习模型在中文文本情感分析研究中取得了不错的成绩。然而仅使用词向量模型作为文本表示进行模型学习时,会丢失当前词的情感信息。故论文提出一种基于情感词典结合双向长短期记忆网络和注意力机制的文本情感分析模型SABLSTM。该模型在酒店数据集上的分类准确率是93.17%,比仅结合了注意力机制的双向长短期记忆网络模型的准确率提升了1.56%。由此可见,以情感词典作为先验知识进行模型训练,可以提升中文文本情感分析任务的效果。

基于改进TF-IDF与BERT的领域情感词典构建方法

领域情感词典的构建是领域文本情感分析的基础。现有的领域情感词典构建方法存在所筛选候选情感词冗余度高、情感极性判断失准、领域依赖性强等问题。为了提高所筛选候选情感词的领域性和判断领域情感词极性的准确程度,提出了一种基于改进词频-逆文档频率(TF-IDF)与BERT的领域情感词典构建方法。该方法在筛选领域候选情感词阶段对TF-IDF算法进行改进,将隐含狄利克雷分布(LDA)算法与改进后的TF-IDF算法结合,进行领域性修正,提升了所筛选候选情感词的领域性;在候选情感词极性判断阶段,将情感倾向点互信息算法(SO-PMI)与BERT结合,利用领域情感词微调BERT分类模型,提高了判断领域候选情感词情感极性的准确程度。在不同领域的用户评论数据集上进行实验,结果表明,该方法可以提高所构建领域情感词典的质量,使用该方法构建的领域情感词典用于汽车领域和手机领域文本情感分析的F1值分别达到78.02%和88.35%。

融合多特征和表情情感词典的性别对立言论识别方法

为识别相关极端言论,提出了一种融合多特征和表情情感词典的性别对立言论识别方法。首先,使用BERT(Bidirectional encoder representation from transformer)提取输入文本的字符特征,并使用Word2Vec提取输入文本中五笔、郑码以及拼音3个方面的特征;然后,将这4个方面的特征进行融合,再输入到Bi-GRU(Bi-directional gated recurrent unit)网络中学习更深层次的语义信息;最后,通过全连接层加SoftMax函数计算出情感极性概率,并融合表情情感词典判别输入文本是否为性别对立言论。通过在自行收集的中文性别对立数据集上进行实验,与未加入特征和表情情感词典的方法相比,在F1值上有5.19%的提升。同时,在公开中文情感分析数据集Weibo_senti_100k上进行验证,证明了本方法的泛化性。

引文情感识别研究进展及评述

[目的/意义]引文情感识别是全文本计量时代引文内容分析的重要研究议题之一,它与引文动机/功能识别、引文主题分析、引文摘要自动生成等存在较强的关联性,可为学术评价、知识图谱构建/绘制等问题的解决提供有效的研究支撑,具有较高研究价值。[方法/过程]通过文献调研分析,从引文语料集创建、情感词典使用、情感识别算法应用及存在问题4个方面,对国内外引文情感识别的研究进展进行全面梳理和分析评述。[结果/结论]引文情感识别已从早期的基于情感词典方法发展到当前基于机器学习算法的新阶段,并正由传统机器学习进一步向深度学习推进。亟待解决的主要问题有:(1)缺乏大规模高质量的引文语料集,对引文语料蕴含的特有价值(引文特征)的挖掘利用严重不足;(2)情感词典方法严重依赖情感词典自身的完备性,机器学习算法(分类模型)的参数优化及识别效果仍有提升空间,对两类方法的有机融合利用尚需深入探索;(3)更细粒度和更多维度的引文情感识别研究及相关应用实践有待进一步拓展和深化。

基于位置交互感知网络的多任务情绪原因对抽取方法

情绪原因对抽取任务旨在同时抽取情感子句和原因子句。已有的方法把情绪原因对抽取看作情绪抽取、原因抽取和情绪原因对抽取3个独立的任务,不能有效捕捉到任务之间的联系。此外,现有的两阶段模型存在误差传播问题,并且情绪子句和原因子句间相对位置分布不平衡。文中提出了一个新的基于BERT、情感词典和位置感知交互模块的情绪原因对抽取模型MK-BERT。该模型首先用情感词典增强的BERT进行文本编码;其次,为了解决标签位置不平衡问题,根据情感子句和原因子句间的相对距离设计位置感知交互模块,以捕捉位置信息并构建情绪原因对的特征;最后,通过情绪预测模块和原因预测模块间交互编码,充分挖掘多个任务间的共享信息。在中文情绪原因对抽取数据集上进行实验,结果表明,所提模型可以有效地抽取情绪原因对,并且在位置不平衡样本上取得良好性能。

基于情感词典的情感分析在抑郁中的研究进展(综述)

抑郁是常见的精神障碍之一,早期的识别与筛查是诊断和管理的前提与基础,基于情感词典的情感分析方法通过获取用户在社交媒体中发布的文本构建词典,利用程序语言构建模型,以分析用户的态度、情绪。本文对基于情感词典的情感分析的概况和实施方法进行介绍,分析其在抑郁领域的应用及发展前景,旨在为利用互联网早期识别与筛查抑郁个体提供参考,实现心理健康状态智能监测服务。

基于交互注意力机制的心理咨询文本情感分类模型

心理咨询场景下的情感分类旨在获得咨询者话语的情感倾向,为建立心理咨询AI助手提供支持。现有的方法利用语境信息获取文本情感倾向,但未考虑对话记录中当前句与前向近邻句之间的情感传递。针对这一问题,提出一种基于交互注意力(AOA)机制的心理咨询文本情感分类模型,根据时序对历史情感词分配权重,进而提高分类准确率。利用构建的心理健康情感词典分别提取对话双方的历史情感词序列,再将当前句和历史情感词序列输入到双向长短期记忆(BiLSTM)网络获取对应的特征向量,并利用艾宾浩斯遗忘曲线对历史情感词序列分配权重。通过AOA机制获得惯性特征和交互特征,并结合文本特征输入到分类层计算情感倾向概率。在公开数据集Emotional First Aid Dataset上的实验结果表明,相较于Caps-DGCN(Capsule network and Directional Graph Convolutional Network)模型,所提模型的F1值提高了1.55%。可见,所提模型可以有效提升心理咨询文本的情感分类效果。

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。

基于扩充词典和规则集的突发事件评论情感分类

为准确分析突发事件发生后网民评论文本的情感倾向,提出一种结合扩充情感词典和规则集的情感分析模型。根据词语信息熵筛选领域情感词汇,利用卡方检验判断领域情感词汇的情感极性,得到突发事件领域情感词典;根据文本情感规则集与扩充情感词典计算文本情感值,对低于情感值阈值的文本使用集成学习模型进行二次分类,得到突发事件评论文本的情感类别。通过实验验证了该模型的有效性,为突发事件情感分析提供了可参考的模型和求解算法。

Construction of unsupervised sentiment classifier on idioms resources

Sentiment analysis is the computational study of how opinions, attitudes, emotions, and perspectives are expressed in language, and has been the important task of natural language processing. Sentiment analysis is highly valuable for both research and practical applications. The focuses were put on the difficulties in the construction of sentiment classifiers which normally need tremendous labeled domain training data, and a novel unsupervised framework was proposed to make use of the Chinese idiom resources to develop a general sentiment classifier. Furthermore, the domain adaption of general sentiment classifier was improved by taking the general classifier as the base of a self-training procedure to get a domain self-training sentiment classifier. To validate the effect of the unsupervised framework, several experiments were carried out on publicly available Chinese online reviews dataset. The experiments show that the proposed framework is effective and achieves encouraging results. Specifically, the general classifier outperforms two baselines(a Na?ve 50% baseline and a cross-domain classifier), and the bootstrapping self-training classifier approximates the upper bound domain-specific classifier with the lowest accuracy of 81.5%, but the performance is more stable and the framework needs no labeled training dataset.

融合Skip-gram与R-SOPMI的教育领域情感词典构建

提出一种基于特征融合的细粒度教育领域情感词典构建方法。首先构建了教育领域语料库,包含正式、非正式领域情绪特征;其次提出一种融合特征的领域情绪词典构建方法,在情绪划分基础上识别词的语言概率特征以及统计概率特征,改进情感倾向点互信息,提出用于情绪分类的情感倾向点互信息算法,实现共现多分类情绪划分;最后得到细粒度教育领域情感词典,词典扩充至39 138个情绪词。实验表明:使用所提出方法构建的教育领域情绪词典除情绪“怒”以外,各类别F1综合指标均高于78.09%,整体性能良好。与通用词典相比,宏平均准确率、宏召回率和宏F1分别提升了21.95%、2.50%和13.01%,表明该融合特征方法能有效提取领域特征进而完成细粒度领域词典构建。

方面级情感分析综述

情感分析是自然语言处理领域的重要分支之一。随着时代的发展,为了能从文本数据中提取出更多的情感信息,方面级情感分析在情感分析中的关注度越来越高。首先介绍方面级情感分析的背景知识、相关概念,并从方面抽取和方面情感分类两个子任务角度进行阐述。在方面抽取方面,介绍了基于相似度算法、主题模型和序列标注的相关方法。在方面情感分类方面,介绍了基于情感词典与规则、机器学习和深度学习的相关方法,并整理了方面级情感分析中常用的中英文数据集和情感字典,最后对方面级情感分析目前面临的挑战和未来的发展方向做出总结和展望。