The development of multimodal media compensates for the lack of information expression in a single modality and thus gradually becomes the main carrier of sentiment. In this situation, automatic assessment for sentiment information in multimodal cont...
Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information...
The purpose of this paper is to develop a hybrid model Ukrainian language sentiment analyzer, which should improve the accuracy of the mood definition to expand the Ukrainian language among the instruments on the market. The object of research is the...
At present, the proportion of individual financial investors in China is relatively high, the phenomenon of noisy trading is frequent, and the market system risk caused by the polarization of investor sentiment cannot be ignored. Therefore, exploring...
The rapid growth of user-generated content in the digital space has increased the necessity of properly and interpretively analyzing sentiment and emotion systems. This research paper presents a new hybrid model, HAM (Hybrid Attention-based Model), a...
Emerging research indicates that sentiment analyses of Dubrovnik focus mainly on hotel accommodations and restaurants. However, little attention has been paid to attractions, even though they are an important aspect of destinations and require more c...
Fine-tuning a BERT-Base model for specific tasks, such as sentiment analysis, has become resource-intensive and often requires high computational power and memory. This paper introduces SCALE, a novel resource-efficient fine-tuning method that target...
Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the cont...
Customer sentiment analysis plays a pivotal role in the digital economy by offering comprehensive insights that inform strategic business decisions, optimize digital marketing initiatives, and improve overall customer satisfaction. We propose a large...
Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of an aspect in a given sentence and thus can provide people with comprehensive information. However, many conventional methods need help to discover the linguistic knowledge impli...
In recent years, more and more attention has been paid to text sentiment analysis, which has gradually become a research hotspot in information extraction, data mining, Natural Language Processing (NLP), and other fields. With the gradual popularizat...
As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an...
Stock price prediction has been a subject of significant interest in the financial mathematics field. Recently, interest in natural language processing models has increased, and among them, transformer models, such as BERT and FinBERT, are attracting...
Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose...
Sentiment analysis is a well-known task that has been used to analyse customer feedback reviews and media headlines to detect the sentimental personality or polarisation of a given text. With the growth of social media and other online platforms, lik...
Sentiment analysis is essential for understanding consumer opinions, yet selecting the optimal models and embedding methods remains challenging, especially when handling ambiguous expressions, slang, or mismatched sentiment–rating pairs. This s...
Sentiment analysis (SA) has become an essential tool for analyzing social media content in order to monitor public opinion and support digital analytics. Although transformer-based SA models exhibit remarkable performance, they lack mechanisms to mit...
The increasing frequency of structural failures on construction sites emphasizes the critical role of rigorous supervision in ensuring the quality of both construction processes and materials. Current regulatory frameworks mandate the production of d...
This study explores risk–reward patterns in the US stock market and establishes optimal factor-based investing using the Fama–French five-factor model through market cycles constructed by Shiller’s interest rates and Baker–Wur...
The rapid advancement of large language models (LLMs) has revolutionized natural language processing (NLP), yet fine-tuning these models for domain-specific applications remains a resource-intensive challenge. A novel fine-tuning methodology is odds...
As people increasingly make hotel booking decisions relying on online reviews, how to effectively improve customer ratings has become a major point for hotel managers. Online reviews serve as a promising data source to enhance service attributes in o...
Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. Th...
This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor senti...
With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been com...
Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support...
One of the main tasks in the field of natural language processing (NLP) is the analysis of affective states (sentiment and emotional) based on written text, and attempts have improved dramatically in recent years. However, in studies on the Arabic la...
Accurate forecasting in low-liquidity futures markets is essential for effective trading. This study introduces a hybrid decision-support framework that combines Mini-TAIEX (MTX) futures data with sentiment signals extracted from 13 financial news so...
Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represente...
The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discrim...
The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpos...
In recent years, people have expressed their opinions and sentiments about products, services, and other issues on social media platforms and review websites. These sentiments are typically classified as either positive or negative based on their tex...
Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because ther...
Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (...
The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mob...
An aspect-based sentiment analysis (ABSA) aims to perform a fine-grained analysis of text to identify sentiments and opinions associated with specific aspects. Recently, transformers and large language models have demonstrated exceptional performance...
Dimensional aspect-based sentiment analysis (dimABSA) aims to recognize aspect-level quadruples from reviews, offering a fine-grained sentiment description for user opinions. A quadruple consists of aspect, category, opinion, and sentiment intensity,...
27 July 2021
Online social networks, as popular media and communications tools with their own extensive uses, play key roles in public opinion polls, politics, economy, and even governance. An important issue regarding these networks is the use of multiple source...
The growing use of social media data has opened new avenues for understanding user perceptions and operational inefficiencies in transportation systems. Among the most widely adopted analytical approaches for extracting insights from these data are s...
Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle...
Government may need to launch policies to stabilize real estate prices being away from unusual rise at an unexpected pace through short-term regulations of sales and purchases. Short-term control policies are often not effective immediately after wit...
1 October 2025
Rapid advancements in artificial intelligence and the Internet of Things (IoT) have fueled the growth of furniture, transforming traditional home environments into intelligent living spaces. As consumer adoption accelerates, understanding user concer...
The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote c...
Given the high inflationary pressure in Romania, the aim of this paper is to demonstrate the potential of autoregressive distributed lag (ARDL) models incorporating sentiment analysis to provide better inflation forecasts compared to machine learning...
Sentiment analysis, in the context of digital banking reviews, aims to assess customer satisfaction and support service enhancement. Despite increasing attention to sentiment analysis across domains, Arabic banking reviews remain underexplored. To br...
Online medical forums have emerged as vital platforms for patients to share their experiences and seek advice, providing a valuable, cost-effective source of feedback for medical service management. This feedback not only measures patient satisfactio...
Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuse...
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 formu...
Online product reviews provide valuable insights on user experiences and product qualities. However, issues such as information overload and the limited utilization of review features persist, particularly in personalized rankings for popular items l...
This paper proposes a novel hybrid model for sentiment analysis. The model leverages the strengths of both the Transformer model, represented by the Robustly Optimized BERT Pretraining Approach (RoBERTa), and the Recurrent Neural Network, represented...
The world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population&rsqu...
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