Exploring public perceptions on alternative meat in China from social media data using transfer learning method

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

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Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

10.2196/preprints.22734 ◽

2020 ◽

Author(s):

Oladapo Oyebode ◽

Chinenye Ndulue ◽

Ashfaq Adib ◽

Dinesh Mulchandani ◽

Banuchitra Suruliraj ◽

...

Keyword(s):

Social Media ◽

Natural Language Processing ◽

Language Processing ◽

Social Issues ◽

Public Perceptions ◽

Psychosocial Issues ◽

Social Media Data ◽

Health Related ◽

The Impact ◽

Media Data

BACKGROUND The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. METHODS We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, as well as in reacting to any future pandemics.

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Identifying psychological indexes based on social media data: A machine learning method

Advances in Psychological Science ◽

10.3724/sp.j.1042.2021.00571 ◽

2021 ◽

Vol 29 (4) ◽

pp. 571

Author(s):

Yue SU ◽

Mingming LIU ◽

Nan ZHAO ◽

Xiaoqian LIU ◽

Tingshao ZHU

Keyword(s):

Machine Learning ◽

Social Media ◽

Machine Learning Method ◽

Learning Method ◽

Social Media Data ◽

Media Data

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Mining on Social Media

Advances in Data Mining and Database Management - Web Data Mining and the Development of Knowledge-Based Decision Support Systems ◽

10.4018/978-1-5225-1877-8.ch013 ◽

2017 ◽

pp. 206-222 ◽

Cited By ~ 1

Author(s):

Ambati Venkata Krishna Prasad ◽

Venkata Naresh Mandhala

Keyword(s):

Social Media ◽

Social Network ◽

Sentiment Analysis ◽

Learning Method ◽

Bayes Classifier ◽

Social Media Data ◽

Social Media Mining ◽

Analysis Technique ◽

Media Data ◽

Media Mining

Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. Social media is favored by many users since it is available to individuals without any limitations to share their opinions, educational learning experiences and concerns via their status. Twitter API, twitter4j, is processed for searching the tweets based on the geo location. Student's posts on social network offers us a stronger concern to take decisions concerning the particular education system's learning method of the system. Evaluating knowledge in social media is sort of a difficult method. Bayes classifier are enforced on deep-mined knowledge for analysis purpose to urge the deeper understanding of the information. It uses multi label classification technique as every label falls into completely different classes. Label based measures are mostly taken to research the results and comparing them with the prevailing sentiment analysis technique.

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Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia?

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc8010016 ◽

2022 ◽

Vol 8 (1) ◽

pp. 16

Author(s):

Massimo Regona ◽

Tan Yigitcanlar ◽

Bo Xia ◽

Rita Yi Man Li

Keyword(s):

Social Media ◽

Technology Adoption ◽

Construction Industry ◽

Methodological Approach ◽

Public Perceptions ◽

Social Media Analytics ◽

Social Media Data ◽

The Public ◽

Negative Perceptions ◽

Media Data

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.

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