Sentiment Features Research Articles

Art design integrating visual relation and affective semantics based on Convolutional Block Attention Mechanism-generative adversarial network model.

Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model's superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image.

UxSense: Supporting User Experience Analysis with Visualization and Computer Vision.

Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose UXSENSE, a visual analytics system using machine learning methods to extract user behavior from audio and video recordings as parallel time-stamped data streams. Our implementation draws on pattern recognition, computer vision, natural language processing, and machine learning to extract user sentiment, actions, posture, spoken words, and other features from such recordings. These streams are visualized as parallel timelines in a web-based front-end, enabling the researcher to search, filter, and annotate data across time and space. We present the results of a user study involving professional UX researchers evaluating user data using uxSense. In fact, we used uxSense itself to evaluate their sessions.

The Role and Impact of Social Media Data Analysis in Art Event Planning

Abstract The proliferation of rich social data sources in the big data era offers a valuable opportunity for studying the planning of art events. In this study, we harvested data on art activity planning from social media via microblogging API calls, converting and analyzing this data through a novel methodological framework. Specifically, we employed the Sentiment-Enhanced Deep Graph Convolutional Network (SEDGCN) model to extract and identify sentiment features associated with art activity planning from social media datasets. Sentiments were categorized using the softmax function. Subsequently, our analysis integrated these findings into the art activity planning process. By conducting a correlation analysis between positive and negative sentiments on social media and various elements of art activity planning, we found a significant correlation (p < 0.005) with all six examined elements of art planning. Furthermore, an effectiveness analysis conducted post-implementation of the planned art events revealed predominantly positive emotional responses among attendees. Notably, 325 art professionals reported a sense of healing as a result of their participation. The methodology proposed in this paper for analyzing social media data effectively captures audience emotions, thereby assisting planners in crafting art events that resonate with and fulfill the emotional needs of the audience.

Analysis of value shaping education for college students in the view of ideological education in colleges and universities based on deep learning

Abstract This paper proposes a values sentiment analysis model based on RoBERTa-BiLSTM-Attention based on the mechanism and path of values process evaluation under sentiment analysis. After extracting and presenting the values and sentiment features data, we build the values dataset of college students, finish the training of the model, and obtain the classification results of values and sentiment. The college students’ value identity model is established to analyze the value shaping of education at the national, social, and individual levels. The correlation coefficients of the influencing factors of college students’ values were analyzed, and the correlation coefficients of the influencing factors were less than 0.75, and the p-value was less than 0.01. The hypotheses were put forward, and the standardized regression coefficient of the “social role model” was 0.2658, which was significantly higher than that of the other indicators, and the standardized regression coefficient of “emotional satisfaction” was 0.2658. The regression coefficient of “emotional satisfaction” is 0.1987, second only to cognitive identity. In view of the existence of the problem of values, the proposed political education intervention guidance strategy, after the political education intervention, 71.3625% of the students think that the values of guidance have a great help, but there are still some shortcomings, compared with this paper puts forward the optimization of the values of guidance recommendations.

Prediction of emotion distribution of images based on weighted K-nearest neighbor-attention mechanism.

Existing methods for classifying image emotions often overlook the subjective impact emotions evoke in observers, focusing primarily on emotion categories. However, this approach falls short in meeting practical needs as it neglects the nuanced emotional responses captured within an image. This study proposes a novel approach employing the weighted closest neighbor algorithm to predict the discrete distribution of emotion in abstract paintings. Initially, emotional features are extracted from the images and assigned varying K-values. Subsequently, an encoder-decoder architecture is utilized to derive sentiment features from abstract paintings, augmented by a pre-trained model to enhance classification model generalization and convergence speed. By incorporating a blank attention mechanism into the decoder and integrating it with the encoder's output sequence, the semantics of abstract painting images are learned, facilitating precise and sensible emotional understanding. Experimental results demonstrate that the classification algorithm, utilizing the attention mechanism, achieves a higher accuracy of 80.7% compared to current methods. This innovative approach successfully addresses the intricate challenge of discerning emotions in abstract paintings, underscoring the significance of considering subjective emotional responses in image classification. The integration of advanced techniques such as weighted closest neighbor algorithm and attention mechanisms holds promise for enhancing the comprehension and classification of emotional content in visual art.

PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection

Personality detection identifies personality traits in text. Current approaches often rely on deep learning networks for text representation but they overlook the significance of psychological language knowledge in connecting user language expression to psychological characteristics. Consequently, the accuracy of personality detection is compromised. To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. Firstly, the Bi-LSTM network captures local features of preprocessed sentences to accurately represent the output of sentence sentiment features. Secondly, GCNs map psycholinguistic knowledge, forming semantic networks of entities and relationships. P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. Finally, attention calculation is employed to reinforce key features and weaken irrelevant information. Additionally, a sentence group model captures combined features of related sentences, effectively utilising the text structure to mine sentimental features. Experimental results on multiple datasets demonstrate that the proposed method significantly improves the classification accuracy in personality detection tasks.

Prompted representation joint contrastive learning for aspect-based sentiment analysis

As a fine-grained and challenging subtask in the natural language processing (NLP) community, aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term. In previous ABSA research, most works utilized the pre-trained language model (PLM) as the backbone of their proposed methods, without any specific task-related instructions. Besides, some works focused on learning the dependency information or the external knowledge-enhanced dependency information separately, which lacked the exploitation of the mutual interaction between the normal dependency and knowledge-enhanced dependency. Therefore, we propose a novel ABSA method namely prompted representation joint contrastive learning enhanced graph convolutional networks (PRCL-GCN) to strengthen the robustness of the ABSA model. Specifically, to achieve the task-oriented contextual representation, we design the task-specific prompt template to guide the fine-tuning process of PLM in the ABSA task. And a biaffine attention mechanism is employed to further extract the essential sentiment feature from the prompted representation. Moreover, we introduce the syntax dependency graph as prior knowledge, and construct an affective syntactic dependency graph by injecting the affective knowledge from SenticNet into the graph. Then, we utilize the multi-layer GCNs to process the above two syntactic graphs independently, which aims to learn multi-granularity syntactic features. Subsequently, a novel designed attention variant is leveraged to integrate these syntax features with the guided contextual representation, separately. Eventually, through designing a Kullback–Leibler divergence-based contrastive learning to encourage the model’s learning, we improve the model’s accuracy in modeling contextual representation by integrating the designed dual-ways information. Extensive experiments are conducted on five benchmark datasets, and the outstanding experiment results validate the effectiveness of our proposed model.

Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm.

Image sentiment analysis technology can predict, measure and understand the emotional experience of human beings through images. Aiming at the problem of extracting emotional characteristics in art appreciation, this article puts forward an innovative method. Firstly, the PageRank algorithm is enhanced using tweet content similarity and time factors; secondly, the SE-ResNet network design is used to integrate Efficient Channel Attention (ECA) with the residual network structure, and ResNeXt50 is optimized to enhance the extraction of image sentiment features. Finally, the weight coefficients of overall emotions are dynamically adjusted to select a specific emotion incorporation strategy, resulting in effective bimodal fusion. The proposed model demonstrates exceptional performance in predicting sentiment labels, with maximum classification accuracy reaching 88.20%. The accuracy improvement of 21.34% compared to the traditional deep convolutional neural networks (DCNN) model attests to the effectiveness of this study. This research enriches images and texts' emotion feature extraction capabilities and improves the accuracy of emotion fusion classification.

Design of cultural and creative experience products in scenic spots based on CORE model

Abstract Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the communication power of scenic spots and generate income. In this paper, the CORE model is used as the design concept, and the CORE design process for scenic cultural and creative products is constructed. The plain Bayesian classification algorithm is used to extract review feedback from the product, and the vectorization of the extracted reviews is achieved using the FastText model. The vectorized reviews are input into the logistic regression classification model to obtain sentiment classification results. A fused sentiment feature vector is constructed by combining Apirior and a sentiment dictionary aimed at separating sentiment vector classification. Finally, the experience effect of cultural and creative products is explored as an example of X scenic spot, and the optimization strategy of product design is explored. The attribute feature of X Scenic Spot’s cultural and creative experience product, “color” embraces four emotional attributes, and the corresponding adaptations are 4.77, 5.42, 5.40, and 5.38, respectively. The SDs of the emotional attributes of the existing product are The SDs of the emotional attributes of the existing products are 1.141, 1.025, and 0.184, respectively, and the differences between the product descriptions and the users’ opinions on the perceptual attributes are large, which provides the evaluation correlation in the CORE model. Under the guidance of the CORE model, four aspects of cultural and creative marketing, scenic area IP, consumer needs and product experience dimensions are proposed.

A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews

To overcome limitations in existing methods for sentiment analysis of tourism reviews, the authors propose an image-text multimodal sentiment analysis method (TBGAV). It consists of three modules: image sentiment extraction, text sentiment extraction, and image-text fusion. The image sentiment extraction module employs a pre-trained VGG19 model to capture sentiment features. The text sentiment extraction module utilizes the tiny bidirectional encoder representations from transformers (TinyBERT) model, incorporating the bidirectional recurrent neural network and attention (BiGRU-Attention) module for deeper sentiment semantics. The image-text fusion module employs the dual linear fusion approach to correlate image-text links and the maximum decision-making approach for high-precision sentiment prediction. TBGAV achieves superior performance on the Yelp dataset with accuracy, recall rates, and F1 scores of 77.51%, 78.01%, and 78.34%, respectively, outperforming existing methods. Accordingly, TBGAV is expected to help improve travel-related recommender systems and marketing strategies.

A Joint Fake News Detection Model based on Multi-Features

Text analysis-based models have achieved outstanding results in fake news detection tasks in recent years, which is closely linked to the quantity and quality enhancement of feature information extracted from the text. Drawing upon the existing semantic detection frameworks, studies in this field concentrate on extracting various textual information through a solitary auxiliary feature, text stance feature or sentiment feature. However, it is challenging to depict the general attributes of the text using a single auxiliary feature, which frequently results in missing essential details and leaves problems with stance distortion and emotional resonance. To tackle the problem, this study proposes a joint model for identifying fake news, incorporating numerous textual characteristics. By extracting and blending various aspects of text features, i.e., semantic, stance and sentiment features, a more detailed and effective joint analysis of textual information is attained, resulting in improved performance in fake news detection. On the RumourEval-17 datasets, our model attains the Macro F1 Score of 0.891, surpassing current models for detecting rumors. Additionally, our model obtains a Macro F1 Score of 0.904 on the latest COVID-19 dataset, demonstrating strong competitiveness and promising prospects for fake news detection.

Multi-Channel 2D-CNN Attention Based BiLSTM Method for Low-Resource Ewe Sentiment Analysis

The unavailability of an annotated dataset for a low-resource Ewe language makes it difficult to develop an automated system to appropriately evaluate public opinion on events, news, policies, and regulations. In this study, we collected and preprocessed a low-resourced document-level Ewe sentiment dataset based on social media comments. We used three (3) features learned by word embeddings (Global vectors, word-to-vector, and FastText) rather than hand-crafted features. We further proposed a novel method termed MC2D-CNN+BiLSTM-Attn to detect the exact sentiment feature from the Ewe dataset. Extensive experiments indicate that the proposed method efficiently classifies various sentiments and is superior to benchmark deep learning methods. Results show that in detecting the precise sentiments from raw Ewe textual context, the BiLSTM incorporating Glove outperforms word2vec and FastText embedding with an accuracy of 0.727. Furthermore, Attn+BiLSTM and Multi-channel CNN methods incorporating the word2vec embedding layer perform better than Glove and FastText embedding with an accuracy of 0.848 and 0.896. In contrast, our proposed method with the same word2vec embedding recorded 0.949.

Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.

Product feature sentiment analysis based on GRU-CAP considering Chinese sarcasm recognition

Sentiment analysis based on product online reviews is an important measure in grasping customer’s satisfaction with products, which has the problem of inadequate feature extraction and difficulty in identifying the sentiment polarity of texts with irony. To solve the problem, this paper proposes a novel method of product feature sentiment analysis based on gate recurrent units and capsule neural network (GRU-CAP). Firstly, product online reviews are preprocessed to build a review data set. The feature extraction model of GRU-CAP is then used to extract the product features contained in the data set and generate the corresponding product feature index. After combining the feature index with the data set to form new input data, the recognition of positive/negative emotions, sarcastic/non-sarcastic semantics is carried out through the sentiment analysis model of GRU-CAP. Finally, the recognition results are integrated to achieve fine-grained product feature sentiment analysis. The effectiveness and practicability of the method is validated by an example product-mobile phone on an e-commerce platform. The results show that the method can extract product features and identify sentiment polarity from product online review texts with irony more accurately, so as to provide better product design support for enhancing customer satisfaction with products more effectively.

Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability.

Text sentiment analysis based on BERT-CBLBGA

Current word vector techniques can generate effective word representations from massive text data, and different deep learning models can extract various textual semantic and grammatical features from different perspectives and levels. To extract richer text sentiment features, we propose a fused model of text sentiment classification called BERT-CBLBGA, which combines BERT with multiple feature extraction deep learning models. First, BERT is utilized to generate word embedding of the text, and the obtained feature representations are then fed into TextCNN, BiLSTM, and BiGRU to extract local and global sentiment features of the text. Then we use self-attention to fuse the output of TextCNN, BiLSTM, and BiGRU to enhance the ability to extract text sentiment classification features. Finally, a classifier is employed to classify the extracted features. Experimental results on four different domain text datasets demonstrate that the BERT-CBLBGA outperforms the baseline models in terms of extracting text sentiment features and achieves the best classification performance.

Face-emotion classification guided by deep convolutional neural network

With the rapid development of computer vision and convolutional neural networks, the task of automatic face emotion classification has become a reality. The aim of this study is to improve the underlying neural network model to achieve effective face emotion classification. By presenting a simplified network to generate the recognition model, the author enhances the underlying neural network architecture. The model, in particular, augments the underlying neural network with a convolutional layer, a maximum pooling layer, and a discard layer, and increases the number of neurons in the dense layer from 25 to 128. The convolutional layer allows for the automatic extraction of sentiment features. To decrease the parameters in the feature maps, the maximum pooling layer is applied. The experiments are constructed on the Facial Emotion Recognition 2013 dataset (FER-2013). The streamlined network model improves performance by 6% to 56.32% as compared with the basic neural network model. Numerous experiments show that the proposed streamlined network model can effectively recognize facial emotions. In addition, the author analysis the confusion matrix and finds that the model has weak feedback for aversive emotions. Future research will focus on improving the representation of unclear features such as aversive emotions to enhance model generalization.

Quantum Fuzzy Neural Network for multimodal sentiment and sarcasm detection

Sentiment and sarcasm detection in social media contribute to assessing social opinion trends. Over the years, most artificial intelligence (AI) methods have relied on real values to characterize the sentimental and sarcastic features in language. These methods often overlook the complexity and uncertainty of sentimental and sarcastic elements in human language. Therefore, this paper proposes the Quantum Fuzzy Neural Network (QFNN), a multimodal fusion and multitask learning algorithm with a Seq2Seq structure that combines Classical and Quantum Neural Networks (QNN), and fuzzy logic. Complex numbers are used in the Fuzzifier to capture sentiment and sarcasm features, and QNN are used in the Defuzzifier to obtain the prediction. The experiments are conducted on classical computers by constructing quantum circuits in a simulated noisy environment. The results show that QFNN can outperform several recent methods in sarcasm and sentiment detection task on two datasets (Mustard and Memotion). Moreover, by assessing the fidelity of quantum circuits in a noisy environment, QFNN was found to have excellent robustness. The QFNN circuit also possesses expressible and entanglement capabilities, proving effective in various settings. Our code is available at https://github.com/prayagtiwari/QFNN.

Twitter Data Feature Selection Using Enhanced Genetic Algorithm

Feature selection is a basic critical task in sentiment analysis, especially while analyzing Twitter data for stock market sentiment. This paper proposes an enhanced genetic algorithm (GA) for feature selection utilizing Finance Yahoo stocks data and openly accessible Twitter data. The objective is to distinguish the most relevant features that can successfully anticipate stock market sentiment. The proposed GA integrates methods to enhance the investigation and double-dealing capacities, empowering it to look through a bigger feature space and work on the nature of chosen features. The algorithm starts by introducing a populace of random binary chromosomes, with every chromosome addressing a feature subset. Wellness assessment is performed utilizing sentiment analysis strategies to survey the prescient force of each feature subset. Trial assessment utilizing Finance Yahoo stocks and Twitter data shows that the enhanced GA beats customary GA and PSO strategies concerning exactness and forecast performance. The proposed approach gives important experiences to sentiment analysis and feature selection with regards to stock market sentiment utilizing Twitter data.

Research on sentiment analysis method of opinion mining based on multi-model fusion transfer learning

With the popularity of social media, opinion mining has gradually become a popular research field. Among these fields, sentiment analysis mining is an important research direction in the field of opinion mining. It aims to reveal the public's sentiment tendency, and attitude towards specific topics or events by analyzing text data generated by users on online platforms and digital media. However, the large amount of opinion data usually lacks effective annotation, which limits the learning and construction of opinion models. Therefore, focusing on the problem of the scarcity of labeled data in opinion analysis, this paper proposes a mining method for public opinion sentiment analysis based on multi-model fusion transfer learning, that can make full use of the limited labeled data to improve the learning efficiency of sentiment features by integrating the advantages of different models. Additionally, it introduces a transfer learning strategy to enable the models of the target domains to perform better in the absence of labeled data. Furthermore, the attention mechanism is combined to strengthen the acquisition of key features of the emotional colors and improve the accuracy of sentiment analysis. Specifically, the paper uses the ERNIE model to generate dynamic representations of the text word vectors in the dataset. It also uses TextCNN and BiGRU to construct a joint model for extracting local and overall features of the text word vectors. The parameters of the feature layer of the trained model are migrated to the target domain through transfer learning. The attention mechanism is combined with the model to identify the extreme elements of the sentiment. Finally, the local and overall features are fused to achieve comprehensive mining of public opinion and emotional information. This method can effectively improve the accuracy and generalization of public opinion analysis in cases of data scarcity. In the experimental part, the paper conducts comparisons and analyses in eight aspects: word embedding model, model combination, attention mechanism, transfer learning, source domain dataset, target domain dataset, model training, and baseline model. The four indicators, namely accuracy, precision, recall, and F1-measure are used to evaluate the performance of the method. The experiments are thorough and detailed, demonstrating the effective improvement of opinion mining performance.