Topic Modelling and Opinion Analysis On Climate Change Twitter Data Using LDA And BERT Model.

Uthirapathy, Samson Ebenezar; Domnic, S.

doi:10.1016/j.procs.2023.01.071

Cited by 38 publications

(17 citation statements)

References 3 publications

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“…This study adopted an unsupervised learning approach, using Latent Dirichlet Allocation (LDA) for topic modelling. After a comprehensive review of available techniques, LDA was chosen due to its simplicity and widespread utilization, as evidenced in existing work on X 13,14,15 . Our research primarily focuses on applying a well-documented technique within a novel database, aiming to extract meaningful insights from the data.…”

Section: Topic Modelling (Lda)mentioning

confidence: 99%

Understanding Public Perceptions and Discussions on Fibromyalgia through Social Media: Cross-Sectional Infodemiology Study

Valades,

Montero-Torres,

Lara-Abelenda

et al. 2024

Preprint

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Background: Fibromyalgia is a prevalent condition of unknown etiology, characterized by generalized chronic pain that leads to disability, and has significant direct and indirect costs. The objective of this study was to examine the content and key aspects of tweets pertaining to diseases associated with chronic pain. Methods: We investigated tweets published between January 1, 2018, and December 31, 2022, by English and Spanish-speaking Twitter users, as well as generated retweets. Additionally, emotions were extracted from these tweets and their dissemination was analysed. Similarly, the topics that users most frequently address were compiled. Results: In total, 72,874 tweets were analysed in both English (44,467) and Spanish (28,407). Paraplegia represented 23.3%, with 16,461 classifiable tweets, followed by headache and fibromyalgia, with 15,337 (21.7%) and 15,179 (21.5%) tweets, respectively. Multiple sclerosis generated 14,781 tweets (21%), while the lowest number of tweets was associated with neuropathy, totaling 8,830 tweets (12.5%). The findings revealed that the primary emotions extracted were "fear" and "sadness". Furthermore, the scope and impact of these tweets were investigated through the generated retweets, with those related to headaches being of the highest interest and having the greatest interaction among users. Conclusions: Our findings contribute to understanding the role that social media plays in fostering public awareness of chronic pain and improving patients' comprehension and treatment. Moreover, these results are likely to be applicable to other countries whose languages were not covered in our study.

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Section: Topic Modelling (Lda)mentioning

confidence: 99%

Understanding Public Perceptions and Discussions on Fibromyalgia through Social Media: Cross-Sectional Infodemiology Study

Valades,

Montero-Torres,

Lara-Abelenda

et al. 2024

Preprint

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“…Guo et al [ 19 ] proposed a TSA method, called BJ-LDA, that uses the LDA to perform topic detection and Maximum Entropy to separate aspect words and their corresponding opinion words to describe each brand from a detailed perspective. Uthirapathy and Sandanam [ 11 ] utilized the LDA to detect the topics from Tweets on climate change. Then, they employ the BERT to classify the sentiments.…”

Section: Background Of the Researchmentioning

confidence: 99%

Topic sentiment analysis based on deep neural network using document embedding technique

2023

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Sentiment Analysis (SA) is a domain- or topic-dependent task since polarity terms convey different sentiments in various domains. Hence, machine learning models trained on a specific domain cannot be employed in other domains, and existing domain-independent lexicons cannot correctly recognize the polarity of domain-specific polarity terms. Conventional approaches of Topic Sentiment Analysis perform Topic Modeling (TM) and SA sequentially, utilizing the previously trained models on irrelevant datasets for classifying sentiments that cannot provide acceptable accuracy. However, some researchers perform TM and SA simultaneously using topic-sentiment joint models, which require a list of seeds and their sentiments from widely used domain-independent lexicons. As a result, these methods cannot find the polarity of domain-specific terms correctly. This paper proposes a novel supervised hybrid TSA approach, called Embedding Topic Sentiment Analysis using Deep Neural Networks (ETSANet), that extracts the semantic relationships between the hidden topics and the training dataset using Semantically Topic-Related Documents Finder (STRDF). STRDF discovers those training documents in the same context as the topic based on the semantic relationships between the Semantic Topic Vector, a newly introduced concept that encompasses the semantic aspects of a topic, and the training dataset. Then, a hybrid CNN–GRU model is trained by these semantically topic-related documents. Moreover, a hybrid metaheuristic method utilizing Grey Wolf Optimization and Whale Optimization Algorithm is employed to fine-tune the hyperparameters of the CNN–GRU network. The evaluation results demonstrate that ETSANet increases the accuracy of the state-of-the-art methods by 1.92%.

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“…In order to compare the performance of Bleem model and other models in extracting document elements, this study selected text similarity model (Islamaj R, 2019) [22] , BI CNN (Yin W, 2015) [23] , ABCNN (Type 3) (Yin W, 2016) [24] , match_ Lstm (Wang X, 2021) [25] , Bert (Uthirapathy S E 2023) [5] serve as the baseline.…”

Section: Performance Comparison and Analysis Of Different Modelsmentioning

confidence: 99%

“…1. First, we built a text implication model for document element extraction-Bleem, which is based on the Bert class model (Uthirapathy S E 2023) [5] , combined with the element analysis method of law, to solve the problem of element information homogeneity, while improving the attention mechanism algorithm, and exploring the associated fragments of document sentences and element examples.…”

Section: Introductionmentioning

confidence: 99%

Design of intelligent legal text analysis and information retrieval system based on BERT model

Li,

Wang

2023

Preprint

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The amalgamation of information retrieval systems and soft computing techniques establishes a robust framework to confront the challenges and seize the opportunities presented by the vast expanse of big data. As the volume, diversity, and velocity of data continue to proliferate, further advancements in this domain are poised to significantly contribute to various realms, encompassing healthcare, finance, e-commerce, and scientific research, ultimately propelling innovation and facilitating decision-making in the epoch of big data. The application of artificial intelligence (AI) technology to the analysis and comprehension of legal documents holds the potential to expedite the acquisition of case-specific information by legal researchers, thereby expediting their research endeavors. This paper proposes a legal text analysis and retrieval model, rooted in the Bleem model (Bert-based Legal Paper's Element Extracting Model). Initially, our model employs Bert as the coding layer to extract the semantic information embedded within document sentences and element exemplars. Subsequently, we leverage the Attention mechanism to align the semantic essence of element example sentences with document sentences, while simultaneously computing their respective attention weights. Comparative experiments and attention visualization are then employed to validate the efficacy of the Bleem model. The experimental results corroborate the superiority of the Bleem model in terms of accuracy and F1 scores. The visualization of the attention mechanism effectively reveals the inner workings of the Bleem model and unveils its capacity to explore pertinent fragments within document sentences and element examples. Building upon the aforementioned model, we have devised an intelligent legal text analysis and retrieval system, empowering legal researchers to swiftly acquire pivotal data through case briefs. This application has effectively fostered the integration of legal services within the holistic management of public risks.

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Topic Modelling and Opinion Analysis On Climate Change Twitter Data Using LDA And BERT Model.

Cited by 38 publications

References 3 publications

Understanding Public Perceptions and Discussions on Fibromyalgia through Social Media: Cross-Sectional Infodemiology Study

Understanding Public Perceptions and Discussions on Fibromyalgia through Social Media: Cross-Sectional Infodemiology Study

Topic sentiment analysis based on deep neural network using document embedding technique

Design of intelligent legal text analysis and information retrieval system based on BERT model

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