Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model

In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.

A Contrastive Analysis of English and Chinese Idioms About Color Words & Their Cultural Connotations

There are many idioms related to color words in English and Chinese.The use of color words in idioms adds beauty and vividness to the language.Due to the cultural differences,“color idioms”have gained different cultural connotations with the development of English and Chinese languages.It is of great significance to accurately understand and grasp the meanings and differences of color-related idioms in Chinese and English.This paper intends to analyze and expound the cultural connotations of English and Chinese idioms related to several widely used basic color words with the aim of helping English learners know and use the idioms about color words better.

Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in English.This model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection methods.Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods.Experimental results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

Using Formants to Extract Short Vowels from Arabic Words with (Consonant Vowel)³Structure

Arabic texts suffer from missing short vowels. Arabic Speech Recognition is not as good as English speech recognition due to the short vowels not being recognized. And the Arabic language is unlike the English language in characteristics such as the number of vowels. English has more than 24 vowels that are close to each other in pronunciation. The Arabic language only has three short vowels that are far from each other in utter and measurement, by elongating those short vowels, long vowels arose. Researchers said that the vowels could be recognized using formants. The formants’ measurements of Arabic vowels are far from each other too, so it is possible to recognize them so that Arabic Speech recognition can give more accurate results. The paper applies this idea to the corpus Phonemes of Arabic. It uses the Euclidian distance method to measure the distances between formant values to recognize Arabic from words with a CV3 structure, the Linear Predictive Coding method and MATLAB to develop the programs that will extract the formants and calculate the means of the short vowels by using the corpus to identify the short vowels within words in the corpus. The results showed that if highly qualified readers were chosen to read the Arabic text, then higher rates of recognition of the short vowels involved in words will be achieved. This paper revealed that some of the characteristics of a language can be utilized for vowel recognition or to enhance the existing methods for speech recognition.

基于BERT-BiLSTM-CRF模型的畜禽疫病文本分词研究

针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。

基于主题词向量中心点的K-means文本聚类算法

K-means由于其时间复杂度低运行速度快一直是最为流行的聚类算法之一,但是该算法在进行聚类时需要预先给出聚类个数和初始类中心点,其选取得合适与否会直接影响最终聚类效果。该文对初始类中心和迭代类中心的选取进行大量研究,根据决策图进行初始类中心的选择,利用每个类簇的主题词向量替代均值作为迭代类中心。实验表明,该文的初始点选取方法能够准确地选取初始点,且利用主题词向量作为迭代类中心能够很好地避免噪声点和噪声特征的影响,很大程度上地提高了K-means算法的性能。

基于A-BiLSTM和CNN的文本分类

为解决单一神经网络不能获取准确全局文本信息的问题,提出一种基于A-BiLSTM双通道和优化CNN的文本分类模型。A-BiLSTM双通道层使用注意力机制关注对文本分类贡献值较大的部分,并用BiLSTM提取文本中上下文语义信息;A-BiLSTM双通道层中将两者输出的特征信息融合,得到高级语义;A-BiLSTM双通道层后,使用优化CNN的强学习能力提取关键局部特征,得到最终文本特征表示。分类器输出文本信息的类别。实验结果表明,该模型分类效果优于其它对比模型,具有良好的泛化能力。

基于BICOMB 2.0书目分析系统文献计量分析的我国2014—2023年专科医院培训研究进展

目的分析中国知网数据库中专科医院培训研究热点,通过定量分析文献数据,帮助评估专科医院培训研究领域学术研究的影响力和质量,发现该领域学术研究的趋势。方法以“专科医院培训”为检索词,检索2014—2023近十年中国知网数据库收录的文献,使用BICOMB 2.0书目分析系统进行计量分析,使用统计分析软件SPSS 26.0进行聚类分析。结果共纳入199篇文献,近十年间出现两个研究的重要时间拐点,排名前十名的期刊载文量在4篇以上(含4篇),累计百分比为28.64%。采BICOMB 2.0书目分析系统提取高频关键词15个,占总频次累计百分比为27.52%。应用SPSS 26.0软件进行系统聚类分析得到4个研究热点,即护理人员在职培训的需求、入职前的培训需求、专科医院的培训成效以及医师培训。结论专科医院培训应发挥国家政策战略支撑作用,不断拓宽、完善护理人员和住院医师规培等领域的研究思路,需要进一步细化专科医院管理人员和学科领域研究。随着我国专科医院资源配置的不断优化、专业学科制度建设更为完善,专科医院培训领域的研究方向和内容仍具有较大的潜力和可能性。

基于三角词袋回环检测的激光惯性SLAM算法

回环检测是减少激光惯性同步定位与建图(SLAM)位姿漂移的有效方法,而回环检测的精度和速度是其能否被应用于SLAM的关键因素。基于此,提出了一种基于三角词袋回环检测的激光惯性SLAM算法。首先,通过激光点云的LinK3D特征生成三角描述符,使用三角描述符构建三角词袋,实现实时位置识别与六自由度回环位姿估计。其次,将LinK3D特征用于帧到帧的点云配准,与惯性测量装置(IMU)预积分相结合,实现精确鲁棒的帧间位姿估计。在KITTI数据集上的实验结果表明,与LIO-SAM算法相比,所提SLAM算法的帧间位姿估计方法更加鲁棒,轨迹的平均均方根误差减少29.79%,每次回环约束的平均耗时减少93.53%。实测实验结果表明,与LIO-SAM算法相比,所提算法每次回环约束的平均耗时减少85.15%,室外长距离实验的绝对轨迹误差的均方根误差减少84.36%。

On the Translation of Chinese Four-character Idioms Loaded with Color Words

Chinese four-character idioms loaded with color words are the typical symbol of the Chinese culture and their transition is important to Chinese-English dictionaries.The quality of the dictionary and users' understanding are affected by the correctness and appropriateness of their translation.This paper mainly focuses on the translation of four-character Chinese idioms with color words in New Century Chinese-English Dictionary.The research shows that there are three strategies for Chinese fourcharacter idioms loaded with color words in the dictionaries:literal translation,free translation and the integration of literal translation and annotative translation.

Chinese emotional words in patients with major depressive disorder during a subliminal Stroop task An event-related potential study

Patients with major depressive disorder （MDD） develop a negative cognitive bias, but how they respond to information in Chinese emotional words is unclear. Here we used a Stroop paradigm with subliminal Chinese emotional words to explore the event-related potential components of abnormal emotional processing Jn patients with MDD. The correct rate was similar in MDD and normal control groups, but MDD reaction time was longer than the normal controls, especially to the negative and neutral stimuli. In N270, repeated-measure analysis of variance demonstrated a significant main effect of the relation electrode and valence on peak amplitude and interactions between valence and electrode site. The peak amplitudes of the three kinds of words were different in the two groups （positive 〉 negative 〉 neutral）. The topography of the difference waves indicated that the difference distributed in the frontal and left parietal-temporal sites across the scalp. In N400, there was a significant main effect of the relation electrode and valence on peak amplitude, and the latency showed a main effect of the electrode and an interaction between electrode and group. The amplitudes induced by type of words were significantly different from each other in both groups （positive 〉 negative 〉 neutral）. The topography of the difference waves indicated that the effect of relation type was primarily at left and right frontal and central and left parietal-temporal regions. Both MDD patients and normal controls exhibited significant emotional Stroop effects during the processing of positive/negative Chinese emotional words. MDD patients showed interference in emotional stimuli in early cognitive processing that induced psychological resource intervention during late emotional information processing.

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

Novel Method to Deal with Interval Quadratic Equations via Sign-Variation Analysis

In this article, analytical results are obtained apparently for the first time in the literature, for the lower and upper bounds of the roots of quadratic equations when two or all three coefficients a, b, c constitute an interval, with a method called the sign-variation analysis. The results are compared with the parametrization technique offered by Elishakoff and Miglis, and with the solution yielded by minimization and maximization commands of the Maple software. Solutions for some interval word problems are also provided to edulcorate the methodology. This article only focuses on the real roots of those quadratic equations, complex solutions being beyond this investigation.

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

The Research of Chinese Words Semantic Similarity Calculation with Multi-Information

Text similarity has a relatively wide range of applications in many fields, such as intelligent information retrieval, question answering system, text rechecking, machine translation, and so on. The text similarity computing based on the meaning has been used more widely in the similarity computing of the words and phrase. Using the knowledge structure of the and its method of knowledge description, taking into account the other factor and weight that influenced similarity, making full use of depth and density of the Concept-Sememe tree, an improved method of Chinese word similarity calculation based on semantic distance was provided in this paper. Finally the effectiveness of this method was verified by the simulation results.

结合Word2vec和BiLSTM的民航非计划事件分析方法

安全是民航业的核心主题。针对目前民航非计划事件分析严重依赖专家经验及分析效率低下的问题,文章提出一种结合Word2vec和双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络模型的民航非计划事件分析方法。首先采用Word2vec模型针对事件文本语料进行词向量训练,缩小空间向量维度;然后通过BiLSTM模型自动提取特征,获取事件文本的完整序列信息和上下文特征向量;最后采用softmax函数对民航非计划事件进行分类。实验结果表明,所提出的方法分类效果更好,能达到更优的准确率和F 1值,对不平衡数据样本同样具有较稳定的分类性能,证明了该方法在民航非计划事件分析上的适用性和有效性。

基于局部Transformer的泰语分词和词性标注联合模型

泰语分词和词性标注任务二者之间存在高关联性,已有研究表明将分词和词性标注任务进行联合学习可以有效提升模型性能,为此,提出了一种针对泰语拼写和构词特点的分词和词性标注联合模型。针对泰语中字符构成音节,音节组成词语的特点,采用局部Transformer网络从音节序列中学习分词特征;考虑到词根和词缀等音节与词性的关联,将用于分词的音节特征融入词语序列特征,缓解未知词的词性标注特征缺失问题。在此基础上,模型采用线性分类层预测分词标签,采用线性条件随机场建模词性序列的依赖关系。在泰语数据集LST20上的试验结果表明,模型分词F1、词性标注微平均F1和宏平均F1分别达到96.33%、97.06%和85.98%,相较基线模型分别提升了0.33%、0.44%和0.12%。

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

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%.

一种基于八词位标签的BiLSTM_CRF藏文分词方法

藏文分词是藏语自然语言处理的一项基础性任务,其性能影响藏文自动摘要、自动分类以及搜索引擎等多个方面。基于词位标注的藏文分词方法通常使用四词位标签集,为了更全面地提取特征信息和更深层次的语义信息,该文提出了一种八词位标签集,采用BiLSTM_CRF模型得到一种基于八词位标签的BiLSTM_CRF藏文分词方法。实验结果表明,该方法取得较好的分词效果,在测试数据集上的准确率、召回率和F1值分别达95.07%、95.57%和95.32%。