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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

Computer Systems Science & Engineering 2023年第12期47卷 2741-2757页

作者： Abdelwahed Motwakel Hala J.Alshahrani Jaber S.Alzahrani Ayman Yafoz Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and *** of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so *** is expressed in several means,like facial and speech expressions,gestures,and written *** recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)*** article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this *** presented DHODBN-EC model aims to examine the existence of distinct emotion classes in *** the introductory level,the DHODBN-EC technique pre-processes the tweets at different ***,the word2vec feature extraction process is applied to generate the word embedding *** emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class ***,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN *** extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

关键词： Deer hunting optimization deep belief network emotion classification Twitter data sentiment analysis english corpus

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Determination of Consistency between Quantitative Evaluation and Non-quantitative Evaluation

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Journal of Donghua University(English Edition) 2023年第3期40卷 334-342页

作者：史倩刘国华 School of Computer Science and Technology Donghua UniversityShanghai 201620China

The combination of quantitative evaluation(QE)and non-quantitative evaluation(NQE)is an important evaluation tool in the fields of academic level evaluation(e.g.,EasyChair,Academic paper review form)and internet commodity evaluation(e.g.,Amazon’s review,Feedback).But the inconsistency between QE and NQE greatly reduces the correctness and usability of the ***,it is a necessary task to judge whether QE is consistent with *** this paper,the predicate formula satisfiability problem is firstly reduced in polynomial time to the consistency problem of QE and NQE,and the uncertainty of the consistency problem is *** the approximate solution to the problem is investigated by using a natural language processing method,which performs sentiment analysis on NQE and finally invokes a database query statement to determine whether QE is consistent with *** results shed light on the feasibility of using the natural language processing method to solve undecidable problems.

关键词： quantitative evaluation(QE) non-quantitative evaluation(NQE) satisfiability problem natural language processing sentiment analysis

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An Intelligent Deep Neural sentiment Classification Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1733-1744页

作者： Umamaheswari Ramalingam Senthil Kumar Murugesan Karthikeyan Lakshmanan Chidhambararajan Balasubramaniyan Department of Electronics and Instrumentation Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia Department of Computer Science and Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia Department of Electronics and Communications Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia

A Deep Neural sentiment Classiﬁcation Network(DNSCN)is devel-oped in this work to classify the Twitter data *** attempts to extract the negative and positive sentiments in the Twitter *** main goal of the system is toﬁnd the sentiment behavior of tweets with minimum ambiguity.A well-deﬁned neural network extracts deep features from the tweets *** extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)*** effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets ***,the proposed DNSCN acts as a binary classiﬁer for TSA2 and TDS databases and a multiclass classiﬁer for *** performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold *** show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature *** F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classiﬁcation)and 0.97(three-class classiﬁcation)for the TSA3 *** system provides better a F1 score of 0.99 for the TDS database.

关键词： Deep neural network word embeddings bag-of-words sentiment analysis text classiﬁcation

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Bitcoin price change and trend prediction through twitter sentiment and data volume

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Financial Innovation 2022年第1期8卷 1293-1312页

作者： Jacques Vella Critien Albert Gatt Joshua Ellul Centre for DLT University of MaltaMsida MSD 2080Malta Department of Information and Computing Sciences Utrecht UniversityPrincetonplein 53584 CC UtrechtThe Netherlands

Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or *** the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/*** this paper,we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/*** utilise not only sentiment extracted from tweets,but also the volume of *** present results from experiments exploring the relation between sentiment and future price at different temporal granularities,with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price *** different neural network models are explored and evaluated,one based on recurrent nets and one based on convolutional *** additional model is presented to predict the magnitude of change,which is framed as a multi-class classification *** is shown that this model yields more reliable predictions when used alongside a price trend prediction *** main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy(63%).

关键词： Bitcoin sentiment analysis Prediction methods Cryptocurrencies

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sentiment Lexicon Construction Based on Improved Left-Right Entropy Algorithm

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Journal of Donghua University(English Edition) 2022年第1期39卷 65-71页

作者：于守健王保英卢婷 College of Computer Science and Technology Donghua UniversityShanghai 201620China

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 *** 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 *** 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 *** show that the sentiment analysis based on SLNW performs better than ***,recall and F-measure are improved in both topic and non-topic Weibo data sets.

关键词： sentiment lexicon new word discovery left-right entropy sentiment analysis point mutual information(PMI)

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基于在线评论的预测模型构建研究

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运筹与模糊学 2024年第2期14卷 766页

作者：袁卓越江南大学商学院，江苏无锡

本研究的目的在于将传统的年限数据以及在线评论等非传统数据一起考量，建立会员销量预测模型，提高销售预测的准确性和可靠性。我们收集了哔哩哔哩大会员的历史销售数据和相关的在线评论数据，采用机器学习方法对数据进行分析和建模。... 详细信息

本研究的目的在于将传统的年限数据以及在线评论等非传统数据一起考量，建立会员销量预测模型，提高销售预测的准确性和可靠性。我们收集了哔哩哔哩大会员的历史销售数据和相关的在线评论数据，采用机器学习方法对数据进行分析和建模。实验结果表明，基于在线评论的销量预测模型相比传统的历史销售数据预测模型，在预测准确性和可靠性方面都有了显著的提升。这表明在线评论数据可以为销售预测提供有价值的信息，对企业制定销售策略具有重要意义。本研究的研究成果可为电商平台和其他企业提供指导，也可为相关学科领域的研究提供有益的借鉴。

关键词：在线评论情感分析自回归模型 Online Reviews sentiment analysis Autoregressive Model

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Label distribution for multimodal machine learning

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Frontiers of Computer Science 2022年第1期16卷 33-43页

作者： Yi REN Ning XU Miaogen LING Xin GENG Department of Computer Science and Engineering Southeast UniversityNanjing211189China

Multimodal machine learning(MML)aims to understand the world from multiple related *** has attracted much attention as multimodal data has become increasingly available in real-world *** is shown that MML can perform better than single-modal machine learning,since multi-modalities containing more information which could complement each ***,it is a key challenge to fuse the multi-modalities in *** from previous work,we further consider the side-information,which reflects the situation and influences the fusion of *** recover multimodal label distribution(MLD)by leveraging the side-information,representing the degree to which each modality contributes to describing the ***,a novel framework named multimodal label distribution learning(MLDL)is proposed to recover the MLD,and fuse the multimodalities with its guidance to learn an in-depth understanding of the jointly feature ***,two versions of MLDL are proposed to deal with the sequential *** on multimodal sentiment analysis and disease prediction show that the proposed approaches perform favorably against state-of-the-art methods.

关键词： multimodal machine learning label distribution learning sentiment analysis disease prediction

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Encoding syntactic representations with a neural network for sentiment collocation extraction

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Science China(Information Sciences) 2017年第11期60卷 7-18页

作者： Yanyan ZHAO Bing QIN Ting LIU Department of Media Technology and Art Harbin Institute of Technology Department of Computer Science and Technology Harbin Institute of Technology

sentiment collocation refers to the collocation of a target word and a polarity word. sentiment collocation extraction aims to extract the targets and their modifying polarity words by analyzing the relationships between them. This can be regarded as a basic sentiment analysis task and is relevant in many practical applications. Previous studies relied mainly on the syntactic path, which is used to connect the target word and the polarity word. To deeply exploit the semantic information of the syntactic path, we propose two types of syntactic representation, namely, relation embedding and subtree embedding, to capture the latent semantic features. Relation embedding is used to represent the latent semantics between targets and their corresponding polarity words, and subtree embedding is used to explore the rich syntactic information for each word on the path. To combine the two types of syntactic representations, a neural network is constructed. We use a recursive neural network(RNN) to model the subtree embeddings, and then the subtree embedding and the word embedding are combined as the enhanced word representation for each word in the syntactic path. Finally,a convolutional neural network(CNN) is adopted to integrate the two types of syntactic representations to extract the sentiment collocations from reviews. Our experiments were conducted on six types of reviews, which included product domains(such as cameras and phones) and service domains(such as hotels and restaurants).The experimental results show that our proposed method can accurately capture the latent semantic features hidden behind the syntactic paths that neither the common feature-based methods nor the syntactic-path-based method can handle, and, further, that it significantly outperforms numerous baselines and previous methods.

关键词： sentiment collocation extraction sentiment analysis syntactic representation neural network re cursive neural network(RNN) convolutional neural network(CNN)

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A Text sentiment Classification Modeling Method Based on Coordinated CNN-LSTM-Attention Model

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Chinese Journal of Electronics 2019年第1期28卷 120-126页

作者： ZHANG Yangsen ZHENG Jia JIANG Yuru HUANG Gaijuan CHEN Ruoyu Institute of Intelligent Information Processing Beijing Information Science and Technology University Beijing Laboratory of National Economic Security Early-Warning Engineering

The major challenge that text sentiment classification modeling faces is how to capture the intrinsic semantic, emotional dependence information and the key part of the emotional expression of text. To solve this problem, we proposed a Coordinated CNNLSTM-Attention(CCLA) model. We learned the vector representations of sentence with CCLA unit. Semantic and emotional information of sentences and their relations are adaptively encoded to vector representations of *** used softmax regression classifier to identify the sentiment tendencies in the text. Compared with other methods, the CCLA model can well capture the local and long distance semantic and emotional *** results demonstrated the effectiveness of CCLA model. It shows superior performances over several state-of-the-art baseline methods.

关键词： Coordinated CNN-LSTM-Attention sentiment analysis Text modeling Semantic information

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Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition

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Frontiers of Computer Science 2017年第3期11卷 429-443页

作者： Yang-Yen OU Ta-Wen KUAN Anand PAUL Jhing-Fa WANG An-Chao TSAI Department of Electronic Engineering Cheng Kung University Talnan 701 China Department of Computer Science and Engineering Kyungpook National Univeristy Daegu 702-701 Korea Department of Digital Multimedia Design Tajen University Pingtung 741 China

This work presents a spoken dialog summariza- tion system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of au- tomatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possi- bility and potential of the proposed system.

关键词： spoken dialog summarization keyword extraction natural language processing (NLP) sentiment analysis

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