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BACKGROUND: The proliferation of misinformation on social media is a significant concern due to its frequent occurrence and subsequent adverse social consequences. Effective interventions for and corrections of misinformation have become a focal point of scholarly inquiry. However, exploration of the underlying causes that affect the public acceptance of misinformation correction is still important and not yet sufficient. OBJECTIVE: This study aims to identify the critical attributions that influence public acceptance of misinformation correction by using attribution analysis of aspects of public sentiment, as well as investigate the differences and similarities in public sentiment attributions in different types of misinformation correction. METHODS: A theoretical framework was developed for analysis based on attribution theory, and public sentiment attributions were divided into 6 aspects and 11 dimensions. The correction posts for the 31 screened misinformation events comprised 33,422 Weibo posts, and the corresponding Weibo comments amounted to 370,218. A pretraining model was used to assess public acceptance of misinformation correction from these comments, and the aspect-based sentiment analysis method was used to identify the attributions of public sentiment response. Ultimately, this study revealed the causality between public sentiment attributions and public acceptance of misinformation correction through logistic regression analysis. RESULTS: The findings were as follows: First, public sentiments attributed to external attribution had a greater impact on public acceptance than those attributed to internal attribution. The public associated different aspects with correction depending on the type of misinformation. The accuracy of the correction and the entity responsible for carrying it out had a significant impact on public acceptance of misinformation correction. Second, negative sentiments toward the media significantly increased, and public trust in the media significantly decreased. The collapse of media credibility had a detrimental effect on the actual effectiveness of misinformation correction. Third, there was a significant difference in public attitudes toward the official government and local governments. Public negative sentiments toward local governments were more pronounced. CONCLUSIONS: Our findings imply that public acceptance of misinformation correction requires flexible communication tailored to public sentiment attribution. The media need to rebuild their image and regain public trust. Moreover, the government plays a central role in public acceptance of misinformation correction. Some local governments need to repair trust with the public. Overall, this study offered insights into practical experience and a theoretical foundation for controlling various types of misinformation based on attribution analysis of public sentiment.

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Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of aspects and opinions as candidate spans that contain many invalid and irrelevant spans. This made the model training and prediction more difficult due to noised spans, leading to poor performance. To address this issue, we first propose a novel span-level approach that explicitly considers prior grammatical knowledge to generate possible candidate spans by part-of-speech filtering. In this way, our approach can make the model easier to be trained and achieve higher performance at the test stage. Besides, the quality of span-level representation of aspects and opinions is crucial for predicting their sentiment relation. To build a high-quality span-level representation of aspects and opinions, we first incorporate the contextual embedding of the entire sequence into span-level representations. Then, we introduce an auxiliary loss based on contrastive learning to make a more compact representation of the same polarities. Experimental evaluations on the 14Lap, 14Res, 15Res, and 16Res datasets demonstrate the effectiveness of our model, achieving state-of-the-art performance in span-based triplet extraction.

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Sentiment Analysis (SA) is a subset of Natural Language Processing (NLP) which has become a promising research area enabling the provision of language specific services. Although research in high resource languages such as English and Chinese has achieved promising results, research in low resource African languages such as Sesotho is still in its infancy due to limited text and speech datasets. This study contributes in this regard by availing the Sesotho News (SN) dataset, as an annotated dataset for the SA and Aspect Based Sentiment Analysis (ABSA) tasks. This dataset may be used for NLP research to benefit 1.85 million Sesotho speakers in Lesotho and 11.5 million speakers in South Africa. The dataset includes 4651 headlines for the ABSA task and 2401 headlines for the SA task using Lesotho's orthography of Sesotho. The news headlines were collected from Sesotho online newspapers and then annotated for the ABSA and SA tasks. The Spearman's correlation and Cohen's Kappa Index metrics show that there is good correlation between the annotators, implying that the SN dataset is of gold standard.

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Multimodal aspect term extraction (MATE) and multimodal aspect-oriented sentiment classification (MASC) are two crucial subtasks in multimodal sentiment analysis. The use of pretrained generative models has attracted increasing attention in aspect-based sentiment analysis (ABSA). However, the inherent semantic gap between textual and visual modalities poses a challenge in transferring text-based generative pretraining models to image-text multimodal sentiment analysis tasks. To tackle this issue, this paper proposes a self-adaptive cross-modal attention fusion architecture for joint multimodal aspect-based sentiment analysis (JMABSA), which is a generative model based on an image-text selective fusion mechanism that aims to bridge the semantic gap between text and image representations and adaptively transfer a textual-based pretraining model to the multimodal JMASA task. We conducted extensive experiments on two benchmark datasets, and the experimental results show that our model significantly outperforms other state of the art approaches by a significant margin.

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Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58.

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Aspect-based sentiment analysis (ABSA) is a task of fine-grained sentiment analysis that aims to determine the sentiment of a given target. With the increased prevalence of smart devices and social media, diverse data modalities have become more abundant. This fuels interest in multimodal ABSA (MABSA). However, most existing methods for MABSA prioritize analyzing the relationship between aspect-text and aspect-image, overlooking the semantic gap between text and image representations. Moreover, they neglect the rich information in external knowledge, e.g., image captions. To address these limitations, in this paper, we propose a novel hierarchical framework for MABSA, known as HF-EKCL, which also offers perspectives on sensor development within the context of sentiment analysis. Specifically, we generate captions for images to supplement the textual and visual features. The multi-head cross-attention mechanism and graph attention neural network are utilized to capture the interactions between modalities. This enables the construction of multi-level aspect fusion features that incorporate element-level and structure-level information. Furthermore, for this paper, we integrated modality-based and label-based contrastive learning methods into our framework, making the model learn shared features that are relevant to the sentiment of corresponding words in multimodal data. The results, based on two Twitter datasets, demonstrate the effectiveness of our proposed model.

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End-to-End aspect-based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and identify their sentiment polarities. Although previous research has demonstrated that syntax knowledge can be beneficial for E2E-ABSA, standard syntax dependency parsing struggles to capture the block-level relation between aspect and opinion terms, which hinders the role of syntax in E2E-ABSA. To address this issue, this paper proposes a block-level dependency syntax parsing (BDEP) based model to enhance the performance of E2E-ABSA. BDEP is constructed by incorporating routine dependency syntax parsing and part-of-speech tagging, which enables the capture of block-level relations. Subsequently. the BDEP-guided interactive attention module (BDEP-IAM) is used to obtain the aspect-aware representation of each word. Finally the adaptive fusion module is leveraged to combine the semantic-syntactic representation to simultaneously extract the aspect term and identify aspect-orient sentiment polarity. The model is evaluated on five benchmark datasets, including Laptop14, Rest _ALL, Restaurant14, Restaurant15, and TWITTER, with F1 scores of 62.67%, 76.53%, 75.42%, 62.21%, and 58.03%, respectively. The results show that our model outperforms the other compared state-of-the-art (SOTA) methods on all datasets. Additionally, ablation experiments confirm the efficacy of BDEP and IAM in improving aspect-level sentiment analysis.

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As a platform of social media with high activity, Twitter has seen the discussion of many hot topics related to the COVID-19 pandemic. One such is the COVID-19 vaccination program, which has skeptics in several religious, ethnic, and socioeconomic groups, and Indonesia has one of the largest populations of various ethnicities and religions of countries worldwide. Diverse opinions based on skepticism about the effectiveness of vaccines can increase the number of people who refuse or delay vaccine acceptance. Therefore, it is important to analyze and monitor stances and public opinions on social media, especially on vaccine topics, as part of the long-term solution to the COVID-19 pandemic. This study presents the Indonesian COVID-19 vaccine-related tweets data set that contains stance and aspect-based sentiment information. The data were collected monthly from January to October 2021 using specific keywords. There are nine thousand tweets manually annotated by three independent analysts. We annotated each tweet with three labels of stance and seven predetermined aspects related to Indonesian COVID-19 vaccine-related tweets: services, implementation, apps, costs, participants, vaccine products, and general. The dataset is useful for many research purposes, including stance detection, aspect-based sentiment analysis, topic detection, and public opinion analysis on Twitter, especially on the policies regarding the prevention of pandemics.

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Graph neural networks (GNNs) have achieved remarkable performance in personalized recommendation, for their powerful data representation capabilities. However, these methods still face several challenging problems: (1) the majority of user-item interaction graphs only utilize the interaction information, which cannot reflect the users' specific preferences for different aspects, making it difficult to capture user preferences in a fine-grained manner. (2) there is no effective way to integrate multi-aspect preferences into a unified model to capture the comprehensive user interests. To address these challenges, we propose a Multi-Aspect enhanced Graph Neural Networks (MA-GNNs) model for item recommendation. Specifically, we learn the aspect-based sentiments from reviews and use them to construct multiple aspect-aware user-item graphs, thus giving the edge practical meaning. And aspect semantic features are introduced into the information aggregation process to adjust users' preferences for different items. Furthermore, we design a routing-based fusion mechanism, which adaptively allocates weights to different aspects to realize the dynamic fusion of aspect preferences. We conduct experiments on four publicly available datasets, and the experimental results show that the proposed MA-GNNs model outperforms state-of-the-art methods. Further analysis proves that fine-grained interest modeling can improve the interpretability of recommendations.

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Patient narratives on social networks contain large amounts of objective information, such as the descriptions of examinations and interventions. Sentiment analysis (SA) models are mostly used to evaluate the conveyed sentiments by patients in these narratives to assess positive or negative clinical outcomes or to judge the impact of a drug or a medical condition. To date, many state-of-the-art SA models often result in false assessment coverage due to the natural medical entities recognition deficiency and ambiguity problem. In this work, we propose a semisupervised-based neural sense disambiguation approach that helps to substantially define ambiguities, their levels, and the relational mappings between biomedical targets and dependencies for accurate aspect-based sentiment prediction. Three main modules are proposed: (1) generate a sentiment value based on extracted concepts and their synsets, (2) encode the representations of the contextual senses and sentiment inputs, and (3) estimate an aspect-based sentiment weight based on the context-dependency sentiment units vs. the biomedical sense. Both intrinsic and extrinsic evaluation proved how the proposed method have succeeded in pruning contextual sense feature generation and showed a strong agreement for biomedical data property parameterization and ambiguity type extraction. Thus, the model offers a significant rate of discrimination of biomedical natural concept senses by critically analysing constraints from conjunctions of positive or negative contextual semantics. A total of 21% of the vocabulary is drug names, 11% is a multiword drug reaction expressions, 7% is disease symptoms, and 5% is disease-related concepts such as symptoms and related therapy terms. Furthermore, the experiments on a multisource data from Twitter and health-related forums have overshadowed sentiment assessment and achieved an accuracy of 0.91 regarding concepts-based biomedical aspects. These results provide fresh insights into how to investigate biomedical knowledge, e.g., Medical Subject Headings (MeSH) and PubMed, to clarify the correspondence of various biomedical descriptive entities, definitions, and data properties from shared medication-related content.

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User-generated content on various Internet platforms is growing explosively, and contains valuable information that helps decision-making. However, extracting this information accurately is still a challenge since there are massive amounts of data. Thereinto, sentiment analysis solves this problem by identifying people's sentiments towards the opinion target. This article aims to provide an overview of deep learning for aspect-based sentiment analysis. Firstly, we give a brief introduction to the aspect-based sentiment analysis (ABSA) task. Then, we present the overall framework of the ABSA task from two different perspectives: significant subtasks and the task modeling process. Finally, challenges are proposed and summarized in the field of sentiment analysis, especially in the domain of aspect-based sentiment analysis. In addition, ABSA task also takes the relations between various objects into consideration, which is rarely discussed in the previous work.

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Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets.

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BACKGROUND: The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. OBJECTIVE: We aim to investigate Twitter users' attitudes toward COVID-19 vaccination in Canada after vaccine rollout. METHODS: We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination-related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward "vaccination" changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. RESULTS: After applying the ABSA system, we obtained 170 aspect terms (eg, "immunity" and "pfizer") and 6775 opinion terms (eg, "trustworthy" for the positive sentiment and "jeopardize" for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to "vaccine distribution," "side effects," "allergy," "reactions," and "anti-vaxxer," and positive sentiments related to "vaccine campaign," "vaccine candidates," and "immune response." These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the "anti-vaxxer" population that used negative sentiments as a means to discourage vaccination and the "Covid Zero" population that used negative sentiments to encourage vaccinations while critiquing the public health response. CONCLUSIONS: Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.

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The COVID-19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India-specific COVID-19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short-term memory, Bi-directional long short-term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

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The Covid pandemic has become a serious public health challenge for people across India and other nations. Nowadays, people rely on the online reviews being shared on different review sites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, since these reviews are large in number and are unstructured, patients struggle to get accurate and reliable information about the hospitals, due to which they end up taking admission into a hospital which might not be appropriate for the specific treatment they require. This paper employs the use of sentiment analysis to understand various online reviews of hospitals and provide valuable information to the patients. Approximately 30,000 + reviews were collected from more than 500 hospitals. The broad objective of the study is to give the patients a comprehensive and descriptive rating of the hospitals based on the online reviews given by different patients. In addition to providing a comprehensive summary, the study has conducted aspect-based analysis where it compares the hospitals based on four different aspects of the hospital viz. "Doctors' services", "Staff's services", "Hospital facilities", and "Affordability". The database containing aspect-based ratings of the hospitals will be of great value to the patients by allowing them to compare and select the best hospital based on the optimum fit of the aspects of their preference.

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The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document's repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.

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Due to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format. Aspect-Based Sentiment Analysis mines the aspects of a product from the reviews and further determines sentiment for each aspect. In this work, two methods for aspect extraction are proposed. The datasets used for this work are SemEval restaurant review dataset, Yelp and Kaggle datasets. In the first method a multivariate filter-based approach for feature selection is proposed. This method support to select significant features and reduces redundancy among selected features. It shows improvement in F1-score compared to a method that uses only relevant features selected using Term Frequency weight. In another method, selective dependency relations are used to extract features. This is done using Stanford NLP parser. The results gained using features extracted by selective dependency rules are better as compared to features extracted by using all dependency rules. In the hybrid approach, both lemma features and selective dependency relation based features are extracted. Using the hybrid feature set, 94.78% accuracy and 85.24% F1-score is achieved in the aspect category prediction task.

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BACKGROUND: Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns. OBJECTIVE: We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada. METHODS: We analyzed COVID-19-related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19-related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. RESULTS: Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, "vaccines," "economy," and "masks") and 60 opinion terms such as "infectious" (negative) and "professional" (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. CONCLUSIONS: Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19-related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

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Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this article, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This article provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.

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Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public's opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.