The Internet is becoming an increasingly important part of our lives. Internet users share personal information and opinions on social media webs expressing their feelings, judgments, feelings or emotions easy. Text mining and information retrieval techniques allow us to explore all this information and discover what the authors' opinions, claims, or assertions are. A general overview of sentiment analysis' current approaches and its future challenges, providing basic information on their current trends, is made throughout this chapter.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/978-1-4939-1985-7_8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Mining genomic regions associated with stomatal traits and their candidate genes in bread wheat through genome-wide association study (GWAS).
Theor Appl Genet
January 2025
State Key Laboratory of Crop Stress Resistance and High-Efficiency Production and College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China.
112 candidate quantitative trait loci (QTLs) and 53 key candidate genes have been identified as associated with stomatal traits in wheat. These include bHLH, MADS-box transcription factors, and mitogen-activated protein kinases (MAPKs). Stomata is a common feature of the leaf surface of plants and serve as vital conduits for the exchange of gases (primarily CO₂ and water vapor) between plants and the external environment.
View Article and Find Full Text PDFText mining of practical disaster reports: Case study on Cascadia earthquake preparedness.
PLoS One
January 2025
Department of Computer Science, Elizabethtown College, Elizabethtown, PA, United States of America.
Many practical disaster reports are published daily worldwide in various forms, including after-action reports, response plans, impact assessments, and resiliency plans. These reports serve as vital resources, allowing future generations to learn from past events and better mitigate and prepare for future disasters. However, this extensive practical literature often has limited impact on research and practice due to challenges in synthesizing and analyzing the reports.
View Article and Find Full Text PDFReal-time monitoring and prediction of remote operator fatigue in plateau deep mining based on dynamic Bayesian networks.
Sci Rep
January 2025
Yunnan Diqing Non-Ferrous Metals Co., Ltd, Yunnan, 674400, China.
Fatigue can cause human error, which is the main cause of accidents. In this study, the dynamic fatigue recognition of unmanned electric locomotive operators under high-altitude, cold and low oxygen conditions was studied by combining physiological signals and multi-index information. The characteristic data from the physiological signals (ECG, EMG and EM) of 15 driverless electric locomotive operators were tracked and tested continuously in the field for 2 h, and a dynamic fatigue state evaluation model based on a first-order hidden Markov (HMM) dynamic Bayesian network was established.
View Article and Find Full Text PDFHealth promotion campaigns using social media: association rules mining and co-occurrence network analysis of Twitter hashtags.
BMC Public Health
January 2025
Victoria University, Institute for Health and Sport and Australian Health Policy Collaboration, Melbourne, Australia.
Background: Social media hashtags play a significant role in increasing the visibility of health information by making it easier for people to explore health-related content. Health promotion campaigns use campaign-specific hashtags to disseminate health-related messages, enabling individuals to access accurate and timely resources and updates. The study aims to discover patterns of connection between hashtags and identify the most influential hashtags used on Twitter in the American Heart Month campaigns.
View Article and Find Full Text PDFPublic Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods.
JMIR Form Res
January 2025
Department of Health Administration, The College of Health Professions, Central Michigan University, Mt Pleasant, MI, United States.
Article Synopsis
- Sentiment analysis is a key method for analyzing text, especially in social media research, where the choice between manual and automated methods is crucial.
- The study compared several sentiment analysis tools, including VADER, TEXT2DATA, LIWC-22, and ChatGPT 4.0, against manually coded sentiment scores from YouTube comments on the opioid crisis, assessing factors like ease of use and cost.
- Findings revealed that LIWC-22 excelled in identifying sentiment patterns, while VADER was best at classifying negative comments, but overall, automated tools showed only fair agreement with manual coding, with ChatGPT performing poorly.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!