Sentiment Polarity Analysis Research Articles

Hierarchical graph contrastive learning framework based on quantum neural networks for sentiment analysis

Existing multi-modal sentiment analysis (MSA) methods typically achieve interaction by connecting different layers or designing special structures, but rarely consider the synergistic effects among data. Moreover, most sentiment analysis research tends to focus solely on single sentiment polarity analysis, without considering the intensity and directional attributes of emotions. Addressing these issues, we propose a framework called Hierarchical Graph Contrastive Learning based on Quantum Neural Network (HGCL-QNN) to remedy these shortcomings. Specifically, a graph structure is established within and between modalities. In the quantum fuzzy neural network module, fuzzy quantum encoding is implemented by using complex-valued, then quantum superposition and entanglement are utilized to consider the intensity and directional attributes of emotions while analyzing emotional polarity. In the quantum multi-modal fusion neural network module, methods such as amplitude encoding and quantum entanglement are employed to further integrate information from different modalities, thereby enhancing the model's power to express emotional information. To enhance the model's understanding of fine-grained and global features, and to better align and integrate features from different modalities, hierarchical graph contrastive learning is employed on different levels. The experimental results demonstrate that HGCL-QNN outperforms the existing baseline methods on MOSI and MOSEI datasets, achieving significant efficacy improvements.

Tourism Development through the Sense of UNESCO World Heritage: The Case of Hegra, Saudi Arabia

This study explores the perception of a “sense of place” among tourists visiting Hegra, an archaeological site in northern Saudi Arabia, through reviews on TripAdvisor. The 267 reviews on TripAdvisor between 2020 and 2023 were analyzed using the VADER sentiment polarity analysis tool and object modeling using the NMF machine learning algorithm. The results highlight positive factors linked to the history and uniqueness of the place while showing some critical issues related to isolation, cost, privatization, and competitiveness. The originality of the research lies in the type of case study chosen, an archaeological site of a country that has recently opened its doors to tourism, and in the pragmatic nature of the investigation, oriented towards the search for possible solutions to be adopted in terms of heritage management based on the feedback received for the development of the tourist destination.

Aspect-Based Sentiment Analysis of Specific Targets

Different from traditional sentiment analysis tasks, Aspect-Based Sentiment Analysis (ABSA) aims to automatically identify the emotional polarity of different aspects in the same sentence, which helps to mine users' more delicate emotional expressions for different targets and has become a research hotspot in the field of natural language processing in recent years. Thanks to the rapid development of attention-based deep neural network models, the accuracy of aspect sentiment analysis has continuously made breakthroughs. However, existing works pay little attention to the performance bounds of different application scenarios, such as different text topics or text lengths. In response to the above issues, this paper conducts a comparative analysis of several classic end-to-end neural network models to explore topic-level sentiment polarity analysis. By quantitatively evaluating the accuracy and F1 score of each model at different fixed text lengths, this paper summarizes the strengths, weaknesses, and different aspects of each model's efficiency in use. In addition, this paper also discusses the existing problems with these models and puts forward suggestions for improvement, which can provide some new insights into the research of ABSA.

Emotional Behavior Analysis of Music Course Evaluation Based on Online Comment Mining

This study investigates the method of analyzing emotional tendencies in music courses and its application in lesson plan evaluation. Using a weighted method to analyze emotional tendencies in music curriculum, the study compares the results with existing literature, demonstrating the superior accuracy of the proposed method. To evaluate lesson plan quality, a combination of self-assessment, mutual evaluation, group evaluation, and the middle school music lesson plan evaluation form is recommended for comprehensive assessment. The study's method for comment polarity achieves an accuracy rate of 69.19%, significantly outperforming other methods. Additionally, improvements in lexical feature extraction reduce computation complexity and interference factors in sentiment polarity analysis. In conclusion, this study offers valuable insights for enhancing teaching effectiveness, lesson plan quality, and understanding course feedback.

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis.

Comparing COVID-19 vaccine passports attitudes across countries by analysing Reddit comments

Topic mining and sentiment polarity analysis together can adequately represent the topics and attitudes of users. The goal of this article is to use Reddit’s location-based subreddits to look at country-level differences in attitudes towards COVID-19 vaccine passports. We used sentiment analysis and latent topic modelling on textual data obtained from 18 Reddit communities concentrating on COVID-19 vaccine passports from 1 January 2021 to 28 February 2022 to study COVID-19 vaccine passports–related discussion on Reddit. To discover changes in sentiment and latent topics, 11,168 comments were aggregated and examined by month. The number of comments on postings from country-specific subreddits was positively proportional to the number of new COVID-19 cases reported each day. The more subjective expressions and positive/negative interpretations occurred after July 2021. Communities indicated more positive sentiment than negative sentiment towards vaccine passports–related topics, according to polarity analysis. Topic modelling found that community members were concerned about a variety of concerns related to their socioeconomic status. Throughout the topic modelling, keywords suggesting people’s privacy concerns and acceptance of various COVID-19 control methods were found. The use of public opinion and topic modelling to analyse vaccine passports could help with important global health informatics concerns associated with their socioeconomic status.

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media.

As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern—especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens.

Research on Dynamic Political Sentiment Polarity Analysis of Specific Group Twitter Based on Deep Learning Method

The article introduce a method to analyzing the dynamic political sentiment polarity of American politicians’ tweets within a fixed period of time to assist analysts in judging the direction of American politics and the future trend of China-US relations. This research is applied in the field of think tanks or intelligence analysis. The service targets are intelligent analysts, and the data is the tweet text data sent by a specific group in a fixed time period. We propose an architecture that combines multiple deep learning models and use a dedicated tweet data set to construct a specific group to obtain an sentiment polarity multi-classifier, and then introduce the time characteristics of tweets, and finally obtain the dynamic political sentiment polarity of politicians. The US politician tweets data set proposed in this article verifies that the proposed comprehensive architecture is better than traditional deep learning methods in this task, and the accuracy of the classifier verification set reaches 80.66%. According to the sentiment polarity judgments of 20 US governors and senators, the success rate is 75%. The analysis of individual dynamic political sentiment polarity can provide effective help and intelligence support for analysts. The method in this paper effectively uses a variety of deep learning techniques to assist analysts to obtain more accurate dynamic political sentiment polarity from massive Twitter text data.

Applying sentiment analysis to automatically classify consumer comments concerning marketing 4Cs aspects

With the rapid development of science and technology, consumers are used to searching online for evaluations before purchasing products. Manufacturers can also utilize such information like users’ usage habits, browsed websites, comments, messages, etc. to formulate marketing strategies suitable for their products. Several researches developed opinion mining on predicting the polarity of consumers’ comments, but few of them were from marketing point of view. In this regards, this study looks to establish an automated way to collect and analyze consumers’ comments in social networks, automatically classify them into marketing 4Cs and non-marketing categories from a large number of consumer comments, and divide the category of marketing 4Cs articles into four types of attribute dimensions to analyze emotional polarity. Based on the marketing theory of 4Cs and LDA topic analysis, this study extracted the characteristic keywords from the collected consumer reviews for corpus classification and sentiment polarity analysis. This study further establishes a feature keyword library for specific fields, hoping to improve the accuracy of sentiment analysis through these keywords, simplify the process of consumers’ searches for product evaluations, and facilitate consumers to search for helpful target information.

What kind of donor are you? Uncovering complexity in donor identity

AbstractIdentity is a useful lens to understand donation behavior. However, studies have typically conceptualized and examined donor identity as a generic, unidimensional concept. Through in‐depth interviews with 52 blood donors, this study sets out to discover if there is more complexity to donor identity, and what implications this might have for marketing communications, in the context of donation of the self (e.g., blood, organs, time, and effort). We use sentiment polarity and amplification analysis of inductive themes to uncover distinct patterns reflective of four different donor identities. We label these the Savior, Communitarian, Pragmatist, and Elitist, which are underpinned by theories of gift‐giving, sharing, pragmatism, and signaling, respectively. The typology offers a theory‐building mechanism to anticipate the effects of marketing stimuli on donation behavior. We conclude by presenting four theoretical propositions, for which we provide preliminary empirical evidence. The survey data is suggestive of action readiness for donation behavior when a marketing communication message is aligned with its intended donor identity.

Determining the Topic Evolution and Sentiment Polarity for Albinism in a Chinese Online Health Community: Machine Learning and Social Network Analysis.

BackgroundThere are more than 6000 rare diseases in existence today, with the number of patients with these conditions rapidly increasing. Most research to date has focused on the diagnosis, treatment, and development of orphan drugs, while few studies have examined the topics and emotions expressed by patients living with rare diseases on social media platforms, especially in online health communities (OHCs).ObjectiveThis study aimed to determine the topic categorizations and sentiment polarity for albinism in a Chinese OHC, Baidu Tieba, using multiple methods. The OHC was deeply mined using topic mining, social network analysis, and sentiment polarity analysis. Through these methods, we determined the current situation of community construction, identifying the ongoing needs and problems experienced by people with albinism in their daily lives.MethodsWe used the albinism community on the Baidu Tieba platform as the data source in this study. Term frequency–inverse document frequency, latent dirichlet allocation models, and naive Bayes were employed to mine the various topic categories. Social network analysis, which was completed using the Gephi tool, was employed to analyze the evolution of the albinism community. Sentiment polarity analysis was performed using a long short-term memory algorithm.ResultsWe identified 8 main topics discussed in the community: daily sharing, family, interpersonal communication, social life and security, medical care, occupation and education, beauty, and self-care. Among these topics, daily sharing represented the largest proportion of the discussions. From 2012 to 2019, the average degree and clustering coefficient of the albinism community continued to decline, while the network center transferred from core communities to core users. A total of 68.43% of the corpus was emotional, with 35.88% being positive and 32.55% negative. There were statistically significant differences in the distribution of sentiment polarity between topics (P<.001). Negative emotions were twice as high as positive emotions in the social life and security topic.ConclusionsThe study reveals insights into the emotions expressed by people with albinism in the Chinese OHC, Baidu Tieba, providing health care practitioners with greater appreciation of the current emotional support needed by patients and the patient experience. Current OHCs do not exert enough influence due to limited effective organization and development. Health care sectors should take greater advantage of OHCs to support vulnerable patients with rare diseases to meet their evidence-based needs.

A multi-aspect user-interest model based on sentiment analysis and uncertainty theory for recommender systems

This work presents a new multi-aspect user-interest model for recommender systems to improve recommendation and prediction accuracy. We introduce the overall user satisfaction for a product to build a user-interest profile by computing the user-interest levels from multi-aspect reviews. A domain emotional dictionary is built to overcome the gap in quantity between negative and positive words for sentiment polarity analysis. A sentiment analysis model is designed to characterize the users’ sentiment polarity and strength based on uncertainty theory and the domain emotional dictionary. Accordingly, a new multi-aspect user-interest model is proposed by considering the sentiment analysis model with the user-interest profile. Then, our proposed model is applied to recommender systems and experimentally tested on five products of different categories from three e-commerce websites. Our model not only outperforms the traditional and state-of-the-art models on rating prediction tasks but also improves the recommendation accuracy in multiple domains.

Text Classification Using Compression-Based Dissimilarity Measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

An Approach for Computing Sentiment Polarity Analysis of Complex Why-type Questions on Product Review Sites

Opinion questions expect answers from opinionated data available on social web. Opinion why-questions require answers to include reasons, elaborations, explanations for the users' sentiments expressed in the questions. Sentiment analysis has been recently used in answering why type opinion questions. In this paper, we propose an approach to determine the sentiment polarity of complex why type opinion questions that could be expressed in multiple sentences or could have mixed opinions expressed in them. We apply Rhetorical structure theory to determine discourse structure of why type questions. We use such structure to determine sentiment polarity of why type questions and conduct experiments which obtain better results as compared to baseline average scoring methods.

Sentiment Polarity Analysis based multi-dictionary

This paper presents a novel algorithm for Chinese online reviews, which identifies sentiment polarity. To determine the sentence is negative or positive, we extracted opinion words and identified their opinion targets by CRFs and establish the absolute emotional dictionary (AbED), the relative emotional dictionary (ReED), the field of emotional dictionary (FiED) and the field of targets and opinion words dictionary (TfED). With those emotional dictionary, negative dictionary and modified dictionary, we achieved an effective algorithm to discriminate sentiment polarity by multi-string pattern matching algorithm. For evaluation, we used car online reviews, hotel online reviews and computer online reviews which annotated positive or negative. Experimental results show that our proposed method has made a higher precision and recall rate.