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Opinion Mining Using Enriched joint sentiment-topic model

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2023年第1期22卷 313-375页

作者： Osmani, Amjad Mohasefi, Jamshid Bagherzadeh Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Urmia Univ Dept Comp Engn Orumiyeh Iran

sentiment analysis has the potential to significantly impact several fields, such as trade, politics, and opinion extraction. topic modeling is an intriguing concept used in emotion detection. Latent Dirichlet Allocation is an important algorithm in this subject. It investigates the semantic associations between terms in a text document and takes into account the influence of a subject on a word. joint sentiment-topic model is a framework based on Latent Dirichlet Allocation method that investigates the influence of subjects and emotions on words. The emotion parameter is insufficient, and additional factors may be valuable in performance enhancement. This study presents two novel topic models that extend and improve joint sentiment-topic model through a new parameter (the author's view). The proposed methods care about the author's inherent characteristics, which is the most important factor in writing a comment. The proposed models consider the effect of the author's view on words in a text document. The author's view means that the author creates an opinion in his mind about a product/thing before selecting the words for expressing the opinion. The new parameter has an immense effect on model accuracy regarding evaluation results. The first proposed method is author's View-based joint sentiment-topic model for Multi-domain. According to the evaluation results, the highest accuracy value in the first method is equal to 85%. It also has a lower perplexity value than other methods. The second proposed method is Author's View-based joint sentiment-topic model for Single-domain. According to the evaluation results, it achieves the highest accuracy with 95%. The proposed methods perform better than baseline methods with different topic number settings, especially the second method with 95% accuracy. The second method is a version of the first one, which outperforms baseline methods in terms of accuracy. These results demonstrate that the parameter of the author's view

关键词： joint sentiment-topic model Latent Dirichlet allocation sentiment analysis text mining opinion mining topic modeling

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A weakly-supervised graph-based joint sentiment topic model for multi-topic sentiment analysis

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INFORMATION SCIENCES 2022年 609卷 1030-1051页

作者： Zhou, Tao Law, Kris Creighton, Douglas Deakin Univ Fac Sci Engn & Built Environm Sch Engn Geelong Vic 3217 Australia Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic 3217 Australia

Multi-topic sentiment analysis, which aims to identify the topics and classify their corre-sponding sentiment, is of great value in understanding consumers' behaviour and improv-ing services. Because of the high cost of manual annotation of the datasets, topic model -based approaches that model the joint distributions of both topics and sentiments have been studied previously. Some studies proposed models that leverage the prior knowledge derived from the pre-trained word embeddings and have proven effective. However, most of the existing models are based on the assumption that words and topics are conditionally independent, ignoring the dependency relations among them. Additionally, the fine-tuning of the pre-trained word embeddings to incorporate the contextual information is also neglected in these models. This could result in the ambiguous representations of topics. In this paper, we propose a novel weakly-supervised graph-based joint sentiment topic model (W-GJST) that integrates an edge-gated graph convolutional network (E-GCN) into a joint sentiment-topic model. An importance sampling-based training method is proposed to learn the contextual representations of topics and words efficiently. Additionally, a self -training multi-topic classifier is designed for the multi-label topic identification. Experiments on two benchmark datasets demonstrate the superiority of the proposed W-GJST compared to the baseline models in terms of topic modelling, topic identification and topic-sentiment identification. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Multi-topic sentiment analysis Edge-gated graph convolutional network joint sentiment-topic model Self-training Multi-topic sentiment analysis Edge-gated graph convolutional network joint sentiment-topic model Self-training

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sentiment ANALYSIS OF MICROBLOG TEXT BASED ON joint sentiment-topic model 3

SENTIMENT ANALYSIS OF MICROBLOG TEXT BASED ON JOINT SENTIMEN...

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3rd IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Zhang, Hui Liu, Yiqun Ma, Shaoping Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Nanjing Army Command Coll Operat Expt Ctr Nanjing 210045 Jiangsu Peoples R China

ISBN: (纸本)9781479947195

sentiment Analysis of Microb log text is a challenging work. Microblog text is different with general user-generated text, because it often contains some special symbols such as "@ # //" and a lot of emotional symbols. Previous joint sentiment-topic models did not consider these features of Microblog texts and then could not well model them. In this paper, considering the structural features and content features of Microblog text, we present a semi-supervised joint sentiment-topic model (MB-PL-ASUM). This new model uses semi-structured information and emotional symbol information to classify sentiments of Microblog text without labeling them. The experiments of sentiment classification on real Sina Microblog texts show that MB-PL-ASUM outperforms word matching, JTS and ASUM model.

关键词： Microblog Text sentiment Analysis joint sentiment-topic model Emotional Symbol

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sentiment ANALYSIS OF MICROBLOG TEXT BASED ON joint sentiment-topic model

SENTIMENT ANALYSIS OF MICROBLOG TEXT BASED ON JOINT SENTIMEN...

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2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems

作者： Hui Zhang Hqun Liu Shaoping Ma State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Operation Experiment Center Nanjing Army Command College

sentiment Analysis of Microblog text is a challenging *** text is different with general user-generated text,because it often contains some special symbols such as "@ # //" and a lot of emotional *** joint sentiment-topic models did not consider these features of Microblog texts and then could not well model *** this paper,considering the structural features and content features of Microblog text,we present a semi-supervised joint sentiment-topic model(MB-PL-ASUM).This new model uses semistructured information and emotional symbol information to classify sentiments of Microblog text without labeling *** experiments of sentiment classification on real Sina Microblog texts show that MB-PL-ASUM outperforms word matching,JTS and ASUM model.

关键词： Microblog Text sentiment Analysis joint sentiment-topic model Emotional Symbol

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Ratings meet reviews in the monitoring of online products and services

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JOURNAL OF QUALITY TECHNOLOGY 2022年第2期54卷 197-214页

作者： Liang, Qiao Wang, Kaibo Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

User-generated content including both review texts and user ratings provides important information regarding the customer-perceived quality of online products and services. This article proposes a modeling and monitoring method for online user-generated content. A unified generative model is constructed to combine words and ratings in customer reviews based on their latent sentiment and topic assignments, and a two-chart scheme is proposed for detecting shifts of customer responses in dimensions of sentiments and topics, respectively. The proposed method shows superior performance in shift detection, especially for the sentiment shifts in customer responses, based on the results of simulation and a case study.

关键词： control charts joint sentiment-topic model statistical process control user-generated content

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Online sentiment and topic Dynamics Tracking over the Streaming Data

Online Sentiment and Topic Dynamics Tracking over the Stream...

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ASE/IEEE International Conference on Privacy, Security, Risk and Trust / ASE/IEEE International Conference on Social Computing (SocialCom/PASSAT)

作者： He, Yulan Lin, Chenghua Cano, Amparo Elizabeth Open Univ Knowledge Media Inst Milton Keynes Bucks England

ISBN: (纸本)9780769548487;9781467356381

We propose a dynamic joint sentiment-topic model (dJST) which is able to effectively track sentiment and topic dynamics over the streaming data. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information, (1) Sliding window where the current sentiment-topic-word distributions are dependent on the previous sentiment-topic specific word distributions in the last S epochs;(2) Skip model where history sentiment-topic-word distributions are considered by skipping some epochs in between;and (3) Multiscale model where previous long-and short- timescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.

关键词： joint sentiment-topic model dynamic sentiment-topic tracking sentiment analysis sentiment analysis EPOCH Mozilla Streaming multiscale models sliding windows Word dynamics

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