A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Wang, Mingda; Hu, Guangmin

doi:10.3390/info11020092

Cited by 26 publications

(14 citation statements)

References 12 publications

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“…However, Adwan et al [ 32 ] also reviewed a large number of techniques and they found a mix of accuracy scores, with some papers passing 80% accuracy while others still perform below 80% even with new algorithms [ 33 ]. Among those who have improved their accuracy, some only focus on specific politics-related data sets [ 34 ], some propose methods that require a large number of steps [ 35 ], while others address the issues with tweets, such as Twitter-specific language [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Monselise¹,

Chang²,

Ferreira³

et al. 2021

J Med Internet Res

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Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

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Section: Introductionmentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Monselise¹,

Chang²,

Ferreira³

et al. 2021

J Med Internet Res

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show abstract

“…Peng et al [31] established semantic radical embeddings and sentic radical embedding by using the skip-gram model, which incorporated not only semantics at radical and character level, but also sentiment information. Wang et al [32] built a sentiment analysis model fusing tweet-text information and user-relationship information through a three-layer neural structure, which is a word embedding layer, a user embedding layer, and an attentional graph layer. When compared with several other common methods, the performance of this model is improved by more than 5%.…”

Section: Related Workmentioning

confidence: 99%

Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis

et al. 2020

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The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.

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“…An unsupervised attention model was proposed by He et al [29] for sentiment analysis, using attention to remove words that are irrelevant from the sentiment. Wang et al [30] employed attentional-graph neural networks for Twitter sentiment analysis.…”

Section: Text Analysismentioning

confidence: 99%

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Jiang

Chen

et al. 2020

Future Internet

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Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

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A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Cited by 26 publications

References 12 publications

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

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