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sentiment classification via user and product interactive modeling

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Science China(Information Sciences) 2021年第12期64卷 104-116页

作者： Xiabing ZHOU Zhongqing WANG Min ZHOU Qifa WANG Shoushan LI Min ZHANG Guodong ZHOU School of Computer Science and Technology Soochow University

sentiment classification aims to identify the polarity of a given review. Most existing methods consider each review as an individual while ignoring the importance of the user and product information of the given review. A direct way to integrate user and product information is to employ an attention mechanism to learn the local interaction between them. However, local interactions cannot capture the global optimization among user and product information. Therefore, we propose a novel interactive model to integrate both local and global interactions between users and products. In particular, we employ an attention mechanism to learn local interactions between users and products, and construct user and product interactive graphs to model the global interaction of users and products. Empirical evaluation shows that our model outperforms previous state-of-the-art methods significantly by learning the local and global interactions among users' preferences, product characteristics, and reviews.

关键词： product review analysis sentiment classification interactive graph graph convolutional network

sentiment classification Based on Piecewise Pooling Convolutional Neural Network

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Computers, Materials & Continua 2018年第8期56卷 285-297页

作者： Yuhong Zhang Qinqin Wang Yuling Li Xindong Wu School of Computer Science and Information Engineering Hefei University of Technology485 Danxia RoadShushan DistrictHefei230009China School of Computing and Informatics University of Louisiana at Lafayette222 James R.Oliver HallLafayetteLouisiana 70504USA

Recently,the effectiveness of neural networks,especially convolutional neural networks,has been validated in the field of natural language processing,in which,sentiment classification for online reviews is an important and challenging task.Existing convolutional neural networks extract important features of sentences without local features or the feature sequence.Thus,these models do not perform well,especially for transition sentences.To this end,we propose a Piecewise Pooling Convolutional Neural Network(PPCNN)for sentiment classification.Firstly,with a sentence presented by word vectors,convolution operation is introduced to obtain the convolution feature map vectors.Secondly,these vectors are segmented according to the positions of transition words in sentences.Thirdly,the most significant feature of each local segment is extracted using max pooling mechanism,and then the different aspects of features can be extracted.Specifically,the relative sequence of these features is preserved.Finally,after processed by the dropout algorithm,the softmax classifier is trained for sentiment classification.Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods,especially for datasets with transition sentences.

关键词： sentiment classification convolutional neural network piecewise pooling feature extract

Multi-Domain sentiment classification with Classifier Combination

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Journal of Computer Science & Technology 2011年第1期26卷 25-33页

作者：李寿山黄居仁宗成庆 NLP Lab School of Computer Science and TechnologySoochow University Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences

State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing an efficient multi-domain sentiment classification algorithm. Our method employs the approach of multiple classifier combination. In this approach, we first train single domain classifiers separately with domain specific data, and then combine the classifiers for the final decision. Our experiments show that this approach performs much better than both single domain classification approach （using the training data individually） and mixed domain classification approach （simply combining all the training data）. In particular, classifier combination with weighted sum rule obtains an average error reduction of 27.6% over single domain classification.

关键词： sentiment classification multiple classifier system multi-domain learning

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Emphasizing Essential Words for sentiment classification Based onRecurrent Neural Networks

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Journal of Computer Science & Technology 2017年第4期32卷 785-795页

作者： Fei Hu Li Li Zi-Li Zhang Jing-Yuan Wang Xiao-Fei Xu College of Computer and Information Science Southwest University Chongqing 400715 China Network Centre Chongqing University of Education Chongqing 400065 China

With the explosion of online communication and publication, texts become obtainable via forums, chat messages, blogs, book reviews and movie reviews. Usually, these texts are much short and noisy without sufficient statistical signals and enough information for a good semantic analysis. Traditional natural language processing methods such as Bow-of-Word (BOW) based probabilistic latent semantic models fail to achieve high performance due to the short text environment. Recent researches have focused on the correlations between words, i.e., term dependencies, which could be helpful for mining latent semantics hidden in short texts and help people to understand them. Long short-term memory (LSTM) network can capture term dependencies and is able to remember the information for long periods of time. LSTM has been widely used and has obtained promising results in variants of problems of understanding latent semantics of texts. At the same time, by analyzing the texts, we find that a number of keywords contribute greatly to the semantics of the texts. In this paper, we establish a keyword vocabulary and propose an LSTM-based model that is sensitive to the words in the vocabulary; hence, the keywords leverage the semantics of the full document. The proposed model is evaluated in a short-text sentiment analysis task on two datasets: IMDB and SemEval-2016, respectively. Experimental results demonstrate that our model outperforms the baseline LSTM by 1%similar to 2% in terms of accuracy and is effective with significant performance enhancement over several non-recurrent neural network latent semantic models (especially in dealing with short texts). We also incorporate the idea into a variant of LSTM named the gated recurrent unit (GRU) model and achieve good performance, which proves that our method is general enough to improve different deep learning models.

关键词： short text understanding long short-term memory (LSTM) gated recurrent unit (GRU) sentiment classification deep learning

Study of sentiment classification for Chinese Microblog Based on Recurrent Neural Network

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Chinese Journal of Electronics 2016年第4期25卷 601-607页

作者： ZHANG Yangsen JIANG Yuru TONG Yixuan Institute of Intelligent Information Processing Beijing Information Science and Technology University

The sentiment classification of Chinese Microblog is a meaningful topic.Many studies has been done based on the methods of rule and word-bag,and to understand the structure information of a sentence will be the next target.We proposed a sentiment classification method based on Recurrent neural network(RNN).We adopted the technology of distributed word representation to construct a vector for each word in a sentence;then train sentence vectors with fixed dimension for different length sentences with RNN,so that the sentence vectors contain both word semantic features and word sequence features;at last use softmax regression classifier in the output layer to predict each sentence’s sentiment orientation.Experiment results revealed that our method can understand the structure information of negative sentence and double negative sentence and achieve better accuracy.The way of calculating sentence vector can help to learn the deep structure of sentence and will be valuable for different research area.

关键词： Recurrent neural network(RNN) Distributed representation sentiment classification Sentence vector

Chinese sentiment classification Using Extended Word2Vec

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Journal of Donghua University(English Edition) 2016年第5期33卷 823-826页

作者：张胜张鑫程佳军王晖 College of Information System and Management National University of Defense Technology

sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.

关键词： sentiment classification Chinese documents Word2Vec

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Leveraging Vision-Language Pre-Trained Model and Contrastive Learning for Enhanced Multimodal sentiment Analysis

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Intelligent Automation & Soft Computing 2023年第8期37卷 1673-1689页

作者： Jieyu An Wan Mohd Nazmee Wan Zainon Binfen Ding School of Computer Sciences Universiti Sains MalaysiaPenang11800Malaysia Jiangxi University of Applied Science Nanchang330000JiangxiChina

Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities.This limitation is attributed to their training on unimodal data,and necessitates the use of complex fusion mechanisms for sentiment analysis.In this study,we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method.Our approach harnesses the power of transfer learning by utilizing a vision-language pre-trained model to extract both visual and textual representations in a unified framework.We employ a Transformer architecture to integrate these representations,thereby enabling the capture of rich semantic infor-mation in image-text pairs.To further enhance the representation learning of these pairs,we introduce our proposed multimodal contrastive learning method,which leads to improved performance in sentiment analysis tasks.Our approach is evaluated through extensive experiments on two publicly accessible datasets,where we demonstrate its effectiveness.We achieve a significant improvement in sentiment analysis accuracy,indicating the supe-riority of our approach over existing techniques.These results highlight the potential of multimodal sentiment analysis and underscore the importance of considering the intrinsic semantic connections between modalities for accurate sentiment assessment.

关键词： Multimodal sentiment analysis vision–language pre-trained model contrastive learning sentiment classification

Multi-label Emotion classification of COVID–19 Tweets with Deep Learning and Topic Modelling

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Computer Systems Science & Engineering 2023年第6期45卷 3005-3021页

作者： K.Anuratha M.Parvathy Department of Information Technology Sri Sai Ram Institute of TechnologyChennaiTamilnaduIndia Department of Computer Science and Engineering Sethu Institute of TechnologyMaduraiTamilnaduIndia

The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion classification Convolution Neural Network(IBEC-CNN)model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been employed.Analysis of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%nega-tive tweets expressed sadness,fear are of low

关键词： Twitter topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NMF) convolutional neural network(CNN) sentiment classification healthcare

sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

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Computer Systems Science & Engineering 2023年第6期45卷 3231-3246页

作者： E.Susi A.P.Shanthi Department of Computer Science&Engineering Anna UniversityChennai600025Tamil NaduIndia

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

关键词： sentiment drift sentiment classification big data BERT real time data streams twitter