Microblog Sentiment Research Articles

Emoji multimodal microblog sentiment analysis based on mutual attention mechanism.

Emojis, utilizing visual means, mimic human facial expressions and postures to convey emotions and opinions. They are widely used in social media platforms such as Sina Weibo, and have become a crucial feature for sentiment analysis. However, existing approaches often treat emojis as special symbols or convert them into text labels, thereby neglecting the rich visual information of emojis. We propose a novel multimodal information integration model for emoji microblog sentiment analysis. To effectively leverage the emoji visual information, the model employs a text-emoji visual mutual attention mechanism. Experiments on a manually annotated microblog dataset show that compared to the baseline models without incorporating emoji visual information, the proposed model achieves improvements of 1.37% in macro F1 score and 2.30% in accuracy, respectively. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/Emojis/blob/main/weibo-emojis-annotation .

The microblog sentiment analysis based on latent dirichlet allocation and deep learning approaches

To study the application of convolutional neural networks (CNN) in microblog sentiment analysis, a microblog sentiment dictionary is established first. Then, latent Dirichlet allocation (LDA) is proposed for user forwarding sentiment analysis. The sentiment analysis models of CNN and long short-term memory network (LSTM) are established. Experiments are conducted to verify the application effect. The main contributions of this work encompass the establishment of a sentiment lexicon for Weibo, the optimization of two sentiment analysis models, namely CNN and LSTM, as well as the comparison and analysis of the performance of three sentiment analysis approaches: CNN, LSTM, and LDA. The research findings indicate that the CNN model achieves a prediction accuracy of 78.6% and an actual output precision of 79.3%, while the LSTM model attains a prediction accuracy of 83.9% and an actual output precision of 84.9%. The three analysis models all have high sentiment analysis accuracy. Among them, LDA analysis model has the advantages of universality and irreplaceable in text classification, while LSTM analysis model has relatively higher accuracy in sentiment analysis of users forwarding microblog. In short, each sentiment analysis model has its own strengths, and reasonable allocation and use can better classify microblog sentiment.

Microblog sentiment analysis method using BTCBMA model in Spark big data environment

Abstract Microblogs are currently one of the most well-liked social platforms in China, and sentiment analysis of microblog texts can help further analyze the realization of their media value; however, the current task of sentiment analysis based on microblog information suffers from low accuracy due to the large size and high redundancy of microblog data, a microblog sentiment analysis method using Bidirectional Encoder Representation from Transformers (BERT)–Text Convolutional Neural Network (TextCNN)–Bidirectional Gate Recurrent Unit (BiGRU)–Multihead-Attention model in Spark big data environment is proposed. First, the Chinese pre-trained language model BERT is used to convert the input data into dynamic character-level word vectors; then, TextCNN is used to effectively obtain local features such as keywords and pool the filtered features; then, BiGRU is introduced to quickly capture more comprehensive semantic information; finally, a multi-headed attention mechanism is implemented to emphasize the most significant features in order to accomplish the sentiment classification of microblog information task precisely. By comparing the existing advanced models, the proposed model demonstrates an improvement of at least 4.99% and 0.05 in accuracy and F1-score evaluation indexes, respectively. This enhancement significantly enhances the accuracy of microblog sentiment analysis tasks and aids pertinent authorities in comprehending the inclination of individual’s attitude toward hot topics. Furthermore, it facilitates a prompt prediction of topic trends, enabling them to guide public opinion accordingly.

Retraction Note: A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis

Retraction Note: A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis

Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance

Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance

Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths

The identification and analysis of sentiment polarity in microblog data has drawn increased attention. Researchers and practitioners attempt to extract knowledge by evaluating public sentiment in response to global events. This study aimed to evaluate public attitudes towards the spread of COVID-19 by performing sentiment analysis on over 2.1 million tweets in English. The implications included the generation of insights for timely disease outbreak prediction and assertions regarding worldwide events, which can help policymakers take suitable actions. We investigated whether there was a correlation between public sentiment and the number of cases and deaths attributed to COVID-19. The research design integrated text preprocessing (regular expression operations, (de)tokenization, stopwords), sentiment polarization analysis via TextBlob, hypothesis formulation (null hypothesis testing), and statistical analysis (Pearson coefficient and p-value) to produce the results. The key findings highlight a correlation between sentiment polarity and deaths, starting at 41 days before and expanding up to 3 days after counting. Twitter users reacted to increased numbers of COVID-19-related deaths after four days by posting tweets with fading sentiment polarization. We also detected a strong correlation between COVID-19 Twitter conversation polarity and reported cases and a weak correlation between polarity and reported deaths.

Retracted: The Relevance of Microblog Sentiment Analysis on College Students' Growth Development and Management

Retracted: The Relevance of Microblog Sentiment Analysis on College Students' Growth Development and Management

A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis.

More and more individuals are paying attention to the research on the emotional information found in micro-blog comments. TEXTCNN is growing rapidly in the short text space. However, because the training model of TEXTCNN model itself is not very extensible and interpretable, it is difficult to quantify and evaluate the relative importance of features and themselves. At the same time, word embedding can't solve the problem of polysemy at one time. This research suggests a microblog sentiment analysis method based on TEXTCNN and Bayes that addresses this flaw. First, the word embedding vector is obtained by word2vec tool, and based on the word vector, the ELMo word vector integrating contextual features and different semantic features is generated by ELMo model. Second, the local features of ELMo word vector are extracted from multiple angles by using the convolution layer and pooling layer of TEXTCNN model. Finally, the training task of emotion data classification is completed by combining Bayes classifier. On the Stanford Sentiment Classification Corpus data set SST (Stanford Sentiment Classification Corpus Data bank), the experimental findings demonstrate that the model in this paper is compared with TEXTCNN, LSTM, and LSTM-TEXTCNN models. The Accuracy, Precision, Recall, and F1-score of the experimental results of this research have all greatly increased. Their values are respectively 0.9813, 0.9821, 0.9804 and 0.9812, which are superior to other comparison models and can be effectively used for emotional accurate analysis and identification of events in microblog emotion analysis.

Microblog sentiment analysis via user representative relationship under multi-interaction hybrid neural networks

Microblog sentiment analysis via user representative relationship under multi-interaction hybrid neural networks

Microblog sentiment analysis based on deep memory network with structural attention

Microblog sentiment analysis has important applications in many fields, such as social media analysis and online product reviews. However, the traditional methods may be challenging to compute the long dependencies between them and easy to lose some semantic information due to low standardization of text and emojis in microblogs. In this paper, we propose a novel deep memory network with structural self-attention, storing long-term contextual information and extracting richer text and emojis information from microblogs, which aims to improve the performance of sentiment analysis. Specifically, the model first utilizes a bidirectional long short-term memory network to extract the semantic information in the microblogs, and considers the extraction results as the memory component of the deep memory network, storing the long dependencies and free of syntactic parser, sentiment lexicon and feature engineering. Then, we consider multi-step structural self-attention operations as the generalization and output components. Furthermore, this study also employs a penalty mechanism to the loss function to promote the diversity across different hops of attention in the model. This study conducted extensive comprehensive experiments with eight baseline methods on real datasets. Results show that our model outperforms those state-of-the-art models, which validates the effectiveness of the proposed model.

Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, respectively. Then, a convolution operation is used to extract local key features, while cross-channel feature fusion and multi-head self-attention pooling operations are used to extract global semantic information and filter out key data, before using the multi-granularity feature interaction fusion operation to effectively fuse character-level and word-level semantic information. Finally, the Softmax function is used to output the results. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. The results showed that DCCMM outperforms existing advanced sentiment analysis models.

The Relevance of Microblog Sentiment Analysis on College Students' Growth Development and Management

Most of the existing studies focus on the analysis of college student users of blogs, virtual communities, and other online application platforms, while the research on the motivation of micro-blogging college student users focuses on the research of usage motivation. In this paper, we established a multilevel psychological early warning model based on personality, mood, and emotion space and mapped personality, mood, and emotion to accurately simulate the law of human emotion change for the relevance of micro-blog sentiment analysis to college students' growth development and management. The experiment shows that the method effectively realizes the analysis and description of micro-blog emotion; finally, the designed micro-blog emotion early warning system realizes the visual analysis and timely warning of the psychological condition of the observed subjects, which verifies the effectiveness of the research method.

An Ensemble Classification Algorithm of Micro-Blog Sentiment Based on Feature Selection and Differential Evolution

In the face of massive texts, dimensionality reduction algorithm and efficient classification model have become the key steps for sentiment classification of microblogs. X2 statistics and TF-IDF statistics are commonly used dimension reduction methods. When applied to micro-blog sentiment analysis, traditional X2 statistics do not consider the probability of a certain sentiment word in a micro-blog text. TF-IDF weight measure ignores the synonyms in the text of micro-blog. Therefore, this paper proposes a NewChi-TF-IDF feature selection method combining form and semantics. In the classification stage, the generalization performance of single classifier is low. To enhance the generalization performance of Weibo sentiment classification, based on the existing ensemble strategy, differential evolution algorithm is introduced to assign different excitation functions to multiple weak classifiers to train the optimal weight distribution. Thus, the problem that the weight of weak classifier is difficult to determine is solved. Experimental results show that NewChi-TF-IDF feature selection method reduces the dimension, and the generalization ability of the proposed algorithm is enhanced, and the average F-score of the proposed algorithm is improved to a higher degree than that of Ada-All and Vote-All classifiers.

Exploring user historical semantic and sentiment preference for microblog sentiment classification

Microblog text is usually very short, thereby challenging existing sentiment classification methods by providing models with little context. Recently, historical user information has been widely used in many real-world applications, such as recommender systems. However, few research works consider user historical states in the loop of microblog sentiment analysis. In this work, we propose to involve historical user information for microblog sentiment analysis to alleviate the context sparsity problem. In particular, we propose a novel neural microblog sentiment classification method which learns informative representations of microblog posts by exploiting both a user’s contextual information and his/her historical state information. The proposed method consists of four components, i.e., a micropost encoder, a user historical sentiment encoder, a User Historical Semantic Encoder, and a micropost sentiment classification component. Extensive experiments are conducted on real-world data collected from Weibo, and experimental results show that the proposed approach achieves superior performance as compared to state-of-the-art baselines.

Identifying Microblogs for Targeted Contextual Advertising

Micro-blogging sites such as Facebook, Twitter, Google+ present a nice opportunity for targeting advertisements that are contextually related to the microblog content. By virtue of the sparse and noisy text makes identifying the microblogs suitable for advertising a very hard problem. In this work, we approach the problem of identifying the microblogs that could be targeted for advertisements as a two-step classification approach. In the first pass, microblogs suitable for advertising are identified. Next, in the second pass, we build a model to find the sentiment of the advertisable microblog. The systems use features derived from the Part-of-speech tags, the tweet content and uses external resources such as query logs and n-gram dictionaries from previously labeled data.This work aims at providing a thorough insight into the problem and analyzing various features to assess which features contribute the most towards identifying the tweets that can be targeted for advertisements.

Research on sentiment classification of micro-blog short text based on topic clustering

Aiming at the shortage of research on micro-blog short text fine-grained sentiment classification, a fine-grained sentiment classification method about micro-blog short text based on PLSA model and K-means clustering model was proposed. PLSA is used to calculate the probability matrix between documents and topics, words and topics in the corpus. In terms of the probability distribution of words and topics, K-means algorithm is used to cluster the probability distribution of words on topics and merge the similar topics. Based on the sentiment ontology library, emotion recognition is carried out for the merged topics. Then, according to the merged document and topic probability matrix, the document sentiment category is classified. The experimental results show that the sentiment analysis method integrated with PLSA and K-means can obtain higher classification accuracy than the PLSA model method alone.

Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture

This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods.

Sina microblog sentiment in Beijing city parks as measure of demand for urban green space during the COVID-19

Urban green space is an important element of well-being. It is still, however, lacking of research on green space demand analyzing based on social platform data. This research uses social platform data to evaluate people's demand for urban green space under the background of the global COVID-19 pneumonia. Using the micro-blog database of geographical markers and keywords, this research quantified the sentiment expressed inside and outside the parks by means of manual interpretation of characters, pictures and expressions. It addresses the following problems: dose COVID-19 pneumonia reduced people’s demand for green space? Our results showed that despite the restriction of epidemic situation, people still have a great demand for external green space. Older people have less negative sentiment, while younger people (under 18 years) have more negative sentiment. There were significant differences in emotional scores between inside and outside the park. During the period of pneumonia in COVID-19, tourists in Beijing Park generated more positive microblogs inside the park. Landscape and plants are the main elements of people's expression of positive emotions. The emotional scores inside and outside the park are negatively correlated with the new confirmed cases, existing confirmed cases and new deaths.

Hypergraph Learning: Methods and Practices.

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraph learning, inductive hypergraph learning, hypergraph structure updating, and multi-modal hypergraph learning. After that, we present a tensor-based dynamic hypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraph learning development toolkit called THU-HyperG.

Collaborative community-specific microblog sentiment analysis via multi-task learning

Microblog sentiment analysis has become a hot research area due to its wide applications. There are some methods utilizing social context, but they only built a global sentiment analysis model, failing to extract personalized expressions. Some personalized methods have been proposed to deal with this problem, but they suffer from data sparseness and inefficiency. Based on personalized sentiment analysis methods, we exploit social context information and capture users’ variable and distinctive expressions at a community level to handle these problems. In particular, we propose a collaborative microblog sentiment analysis approach. In our approach, two classifiers are constructed. One is the global microblog sentiment analysis model which can exploit the sentiment shared by all users. One is the community-specific microblog sentiment analysis model which can extract sentiment influenced by user personalities. In addition, we extract community similarity knowledge and employ it to improve the learning process of the community-specific sentiment model. Moreover, we incorporate social contexts into this model as regularization to encourage the sharing sentiment between connected microblogs. An accelerated algorithm is introduced to solve our model. Experiments on two real datasets show that our model can advance the performance of microblog sentiment classification effectively and outperform state-of-art methods significantly.