Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent days.ABSA refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text.Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons.In literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and tools.These sources were used for processing and analyzing the Arabic language content like lexicons.However,an important challenge in this domain is the unavailability of sufficient and reliable resources.In this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)technique.The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language.In the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful format.Besides,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of sentiments.Furthermore,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model.This scenario establishes the novelty of current study.The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.

基于最大熵方法的中英文基本名词短语识别

使用了基于最大熵的方法识别中文基本名词短语 在开放语料ChineseTreeBank上 ,只使用词性标注 ,达到了平均 87 4 3% / 88 0 9%的查全率 /准确率 由于 ,关于中文的基本名词短语识别的结果没有很好的可比性 ,又使用相同的算法 ,尝试了英文的基本名词短语识别 在英文标准语料TREEBANKⅡ上 ,开放测试达到了 93 31% / 93 0 4 %的查全率/准确率 ,极为接近国际最优水平 这既证明了此算法的行之有效 。

汉语基本名词短语结构分析模型

本文提出了用词语潜在依存关系分析汉语ｂａｓｅＮＰ结构的模型，它有以下的特点：①将依存语法知识融入概率模型中，使得ｂａｓｅＮＰ结构分析在依存语法知识的指导下进行，其性能优于纯粹的概率模型——相邻模型；②词语潜在依存强度的获取算法是基于ＭＤＬ原则的，在模型建造时既考虑数据拟合性，又考虑模型归纳性，其性能优于基于极大似然原则的词语潜在依存强度获取算法；③词语潜在依存强度获取算法在复杂特征集上进行，可以有效地解决参数估计中的数据稀疏问题．实验结果显示，这个模型对于汉语ｂａｓｅＮＰ结构分析是有效的．

结合句法组成模板识别汉语基本名词短语的概率模型

文中首先给出了汉语基本名词短语（ｂａｓｅＮＰ）的形式化定义，并通过抽取ｂａｓｅＮＰ句法组成模板，显示了这个定义的可操作性．文中指出，句法组成模板只是识别ｂａｓｅＮＰ的必要条件，而非充要条件，仅靠句法组成模板并不能解决ｂａｓｅＮＰ识别中的边界模糊歧义和短语类型歧义问题．据此，把体现ｂａｓｅＮＰ内部组成的句法组成模板与体现上下文约束条件的Ｎ 元模型结合起来，形成了汉语ｂａｓｅＮＰ识别的新模型．实验证明，该模型的性能优于单纯基于词性标记的Ｎ

基于知识图的汉语基本名词短语分析模型

本文提出了一种基于知识图的汉语baseNP分析模型。它以知识图为知识表示方法 ,利用《知网》为语义知识资源 ,采用以语义为主、语法为辅的策略 ,先为短语中的每一个实词构造“词图” ,然后合并“词图”而组成“短语图” ,最后得到一个关于汉语baseNP结构信息和语义信息的知识图。因此它不仅分析了汉语ba seNP结构的内部句法关系 ,而且分析了汉语baseNP结构成分间的语义关系并以知识图的形式表示出了这种语义关系。实验结果表明这个模型对于汉语baseNP的分析是有效的。

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.

基于例子的基本名词短语识别中词语分布相似度的研究

本文提出一种基于例子的基本名词短语的识别模型,并着重讨论了其中的词语相似度度量方法:首先根据词语在限定距离内的同现关系计算词语的关联度,然后利用关联词语和关联度建立词语的语境向量,并基于"相似语境中出现的词语相似"的假设,用词语出现的语境相似度来度量词语分布相似度.实验结果表明,这种基于分布的词语相似度度量方法是基于义类词典的相似度度量方法的重要补充.