scholarly journals Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

scholarly journals Applying for development consent during lockdown: The Sizewell C Nuclear Power Station

2020 ◽  
Vol 22 (2) ◽  
pp. 81-84
Author(s):  
Catherine Ann Caine
Keyword(s):  
Nuclear Power Station ◽  
Nuclear Power ◽  
Internet Access ◽  
Public Consultation ◽  
Limited Resources ◽  
Power Station ◽  
Consultation Process ◽  
The Public ◽  
The Uk

The UK is currently facing unprecedented times as Covid-19 has forced the country into lockdown. However, the recent development consent application from EDF Energy for the Sizewell C Nuclear Power Station provides an opportunity for the planning sector to begin to return to normal. This opinion considers whether it is possible to achieve full public consultation on the Sizewell C Nuclear Power Station application, given the current circumstances that the UK faces due to Covid-19. It is argued that the Planning Inspectorate has not currently taken sufficient action to ensure that members of the public who do not have internet access and those who require library access to make representations are not left out of the process. It is also argued that businesses and non-governmental organisations may also struggle to make representations at a time when they are suffering from limited resources. In conclusion, it is essential that the Planning Inspectorate takes immediate action to ensure that the public consultation process is preserved for applications of this kind while Covid-19 restrictions are in place.

Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Feng-Kwei Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
Response Strategies ◽  
Related Information ◽  
Valence And Arousal ◽  
Public Reaction ◽  
Media Data

PurposeIn recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.Design/methodology/approachThis study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.FindingsThis research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.Originality/valueThis study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

Automatic sentiment analysis of public opinion on nuclear energy

Kerntechnik ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Hong Xu ◽  
Tao Tang ◽  
Baorui Zhang ◽  
Yuechan Liu
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Nuclear Energy ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Innovative Technology ◽  
The Public

Abstract Opinion mining and sentiment analysis based on social media has been developed these years, especially with the popularity of social media and the development of machine learning. But in the community of nuclear engineering and technology, sentiment analysis is seldom studied, let alone the automatic analysis by using machine learning algorithms. This work concentrates on the public sentiment mining of nuclear energy in German-speaking countries based on the public comments of nuclear news in social media by using the automatic methodology, since compared with the news itself, the comments are closer to the public real opinions. The results showed that majority comments kept in neutral sentiment. 23% of comments were in positive tones, which were approximate 4 times those in negative tones. The concerning issues of the public are the innovative technology development, safety, nuclear waste, accidents and the cost of nuclear power. Decision tree, random forest and long short-term memory networks (LSTM) are adopted for the automatic sentiment analysis. The results show that all of the proposed methods can be applied in practice to some extent. But as a deep learning algorithm, LSTM gets the highest accuracy approximately 85.6% with also the best robustness of all.

Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5

2016 ◽  
Vol 40 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Yoosin Kim ◽  
Rahul Dwivedi ◽  
Jie Zhang ◽  
Seung Ryul Jeong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Purchase Intention ◽  
Opinion Mining ◽  
Sales Performance ◽  
Competitive Intelligence ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Media Data

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

scholarly journals Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based

2019 ◽  
Vol 2 (2) ◽  
pp. 29
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Community Services ◽  
Random Forest Algorithm ◽  
The Public ◽  
Online Social Media ◽  
Accuracy Of Measurements

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings. 

Applying classification controls to Internet content in Australia

2015 ◽  
Vol 13 (2) ◽  
pp. 82-97 ◽  
Author(s):  
Shona Leitch ◽  
Matthew Warren
Keyword(s):  
Federal Government ◽  
Public Support ◽  
Public Consultation ◽  
The Internet ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
Internet Filtering ◽  
Qualitative Feedback ◽  
Public Reaction

Purpose – The purpose of this study is to explore Australian public and stakeholders views towards the regulation of the Internet and its content. The federal government called for submissions addressing their proposal, and this paper analyses these submissions for themes and provides clarity as to the Australian public and stakeholders key concerns in regards to the proposed policy. Design/methodology/approach – The paper uses a qualitative approach to analyse the public consultations to the Australian Federal Government. These documents are coded and analysed to determine negative and positive viewpoints. Findings – The research has shown, based upon the analysis of the consultation, that there was no public support for any of the measures put forward, that the Australian Federal Government in its response has not recognised this public feedback and instead has only utilised some of the qualitative feedback obtained through the public consultation process to try to justify its case to proceed with its proposals. Research limitations/implications – The study is focussed on Australia. Practical implications – The paper analyses a proposed national approach to filtering the content of the Internet and discussed the public reaction to such an approach. Social implications – The paper looks at how different parts of Australian society view Internet filtering in a positive or negative manner. Originality/value – The only study that directly looks at the viewpoint of the Australian public.

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

2020 ◽  
Vol 5 (1) ◽  
pp. 86-99
Author(s):  
Runbin Xie ◽  
Samuel Kai Wah Chu ◽  
Dickson Kak Wah Chiu ◽  
Yangshu Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Disease Outbreaks ◽  
Semantic Networks ◽  
Web Crawler ◽  
Public Response ◽  
The Public

AbstractIt is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.

A Survey on Implementation Methods and Applications of Sentiment Analysis

2019 ◽  
pp. 44-58
Author(s):  
Sudheer Karnam ◽  
Valarmathi B. ◽  
Tulasi Prasad Sariki
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Mobile Phones ◽  
Opinion Mining ◽  
Classification Methods ◽  
Analysis Problem ◽  
Public Opinions ◽  
The Public ◽  
Research Problems

Sentiment analysis also called opinion mining, and it studies opinions of people towards products and services. Opinions are very important as the organizations always want to know the public opinions about their products and services. People give their opinions via social media. With the advent of social media like Twitter, Facebook, blogs, forums, etc. sentiment analysis has become important in every field like automobile, medical, film, fashion, stock market, mobile phones, insurance, etc. Analyzing the opinions and predicting the opinion is called sentiment analysis. Sentiment analysis is done using opinion words by classification methods or by sentiment lexicons. This chapter compares different methods of solving sentiment analysis problem, algorithms, its merits and demerits, applications, and also investigates different research problems in sentiment analysis.

scholarly journals Food-system actors’ perspectives on trust: an international comparison

2019 ◽  
Vol 121 (2) ◽  
pp. 561-573 ◽  
Author(s):  
Emma Tonkin ◽  
Annabelle M. Wilson ◽  
John Coveney ◽  
Julie Henderson ◽  
Samantha B. Meyer ◽  
...  
Keyword(s):  
Social Media ◽  
Food System ◽  
Consumer Trust ◽  
Media Industry ◽  
Content Type ◽  
The Public ◽  
The United Kingdom ◽  
Use Of Social Media ◽  
The Uk ◽  
Depth Interviews

Purpose The purpose of this paper is to compare the perspectives of actors who contribute to trust in the food system in four high income countries which have diverse food incident histories: Australia, New Zealand (NZ), the United Kingdom (UK) and the Island of Ireland (IOI), focussing on their communication with the public, and their approach to food system interrelationships. Design/methodology/approach Data were collected in two separate studies: the first in Australia, NZ and the UK (Study 1); and the second on the IOI (Study 2). In-depth interviews were conducted with media, food industry and food regulatory actors across the four regions (n=105, Study 1; n=50, Study 2). Analysis focussed on identifying similarities and differences in the perspectives of actors from the four regions regarding the key themes of communication with the public, and relationships between media, industry and regulators. Findings While there were many similarities in the way food system actors from the four regions discussed (re)building trust in the context of a food incident, their perceptions differed in a number of critical ways regarding food system actor use of social media, and the attitudes and approaches towards relationships between food system actors. Originality/value This paper outlines opportunities for the regions studied to learn from each other when looking for practical strategies to maximise consumer trust in the food system, particularly relating to the use of social media and attitudes towards role definition in industry–regulator relationships.

scholarly journals Mediations of ‘the Refugee Crisis’: The (Ir)reconciliation of Ideological Contradictions in Fortress Europe

2016 ◽  
Vol 9 (4) ◽  
Author(s):  
Alexander Callum Harrison
Keyword(s):  
Social Media ◽  
New Media ◽  
Postcolonial Theory ◽  
Refugee Crisis ◽  
The Public ◽  
Cultural Texts ◽  
Fortress Europe ◽  
The Uk ◽  
Media Narratives ◽  
Public Imagination

In the UK, the summer of 2015 saw the national popular press and public imagination captivated by the ‘refugee crisis’. On both mass and social media sites, public opinion predominantly orientated around two major narratives. On one hand, amidst the dramatic scenes in Calais (as well as elsewhere), the European media worked into a fervour of fear, amid concerns about the ‘swarms’ of migrants purported to be ‘invading’ Europe (Squires 2015, The Telegraph). Taking a theoretical focus through Agamben’s work and giving reverence to where his concerns converge with aspects of postcolonial theory, the following investigation unpacks how the hegemonic (new) media narratives have intensely cycled into an emotionally charged dichotomous discourse obfuscating a multitude of other key considerations. Employing content analysis, this article reads three cultural texts scraped from social media to discuss the ways in which the construction of the refugee identity has been shaped in the public imagination; it calls into question how forefronting the figure of the refugee has foreclosed wider debates about alternative agendas contributing to the processes of Fortressing Europe.

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