scholarly journals How is People's Awareness of “Biodiversity” Measured ?Using Sentiment Analysis and LDA Topic Modeling in the Twitter Discourse Space from 2010 to 2020

2021 ◽  
Author(s):  
Shimon Ohtani
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Biological Diversity ◽  
Public Awareness ◽  
Convention On Biological Diversity ◽  
Emotion Lexicon ◽  
Aichi Biodiversity Targets ◽  
Do So

Abstract The importance of biodiversity conservation is gradually being recognized worldwide, and 2020 was the final year of the Aichi Biodiversity Targets formulated at the 10th Conference of the Parties to the Convention on Biological Diversity (COP10) in 2010. Unfortunately, the majority of the targets were assessed as unachievable. While it is essential to measure public awareness of biodiversity when setting the post-2020 targets, it is also a difficult task to propose a method to do so. This study provides a diachronic exploration of the discourse on “biodiversity” from 2010 to 2020, using Twitter posts, in combination with sentiment analysis and topic modeling, which are commonly used in data science. Through the aggregation and comparison of n-grams, the visualization of eight types of emotional tendencies using the NRC emotion lexicon, the construction of topic models using Latent Dirichlet allocation (LDA), and the qualitative analysis of tweet texts based on these models, I was able to classify and analyze unstructured tweets in a meaningful way. The results revealed the evolution of words used with “biodiversity” on Twitter over the past decade, the emotional tendencies behind the contexts in which “biodiversity” has been used, and the approximate content of tweet texts that have constituted topics with distinctive characteristics. While the search for people's awareness through SNS analysis still has many limitations, it is undeniable that important suggestions can be obtained. In order to further refine the research method, it will be essential to improve the skills of analysts and accumulate research examples as well as to advance data science.

scholarly journals How is People's Awareness of “Biodiversity” Measured ?Using Sentiment Analysis and LDA Topic Modeling in the Twitter Discourse Space from 2010 to 2020

2021 ◽  
Author(s):  
Shimon Ohtani
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Biological Diversity ◽  
Public Awareness ◽  
Convention On Biological Diversity ◽  
Emotion Lexicon ◽  
Aichi Biodiversity Targets ◽  
Do So

Abstract The importance of biodiversity conservation is gradually being recognized worldwide, and 2020 was the final year of the Aichi Biodiversity Targets formulated at the 10th Conference of the Parties to the Convention on Biological Diversity (COP10) in 2010. Unfortunately, the majority of the targets were assessed as unachievable. While it is essential to measure public awareness of biodiversity when setting the post-2020 targets, it is also a difficult task to propose a method to do so. This study provides a diachronic exploration of the discourse on “biodiversity” from 2010 to 2020, using Twitter posts, in combination with sentiment analysis and topic modeling, which are commonly used in data science. Through the aggregation and comparison of n-grams, the visualization of eight types of emotional tendencies using the NRC emotion lexicon, the construction of topic models using Latent Dirichlet allocation (LDA), and the qualitative analysis of tweet texts based on these models, I was able to classify and analyze unstructured tweets in a meaningful way. The results revealed the evolution of words used with “biodiversity” on Twitter over the past decade, the emotional tendencies behind the contexts in which “biodiversity” has been used, and the approximate content of tweet texts that have constituted topics with distinctive characteristics. While the search for people's awareness through SNS analysis still has many limitations, it is undeniable that important suggestions can be obtained. In order to further refine the research method, it will be essential to improve the skills of analysts and accumulate research examples as well as to advance data science.

scholarly journals Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study (Preprint)

2020 ◽  
Author(s):  
Sakun Boon-Itt ◽  
Yukolpat Skunkan
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Public Perception ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Public Awareness ◽  
Good Communication ◽  
Three Stages ◽  
Twitter Users

BACKGROUND COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. OBJECTIVE The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. METHODS Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. RESULTS The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. CONCLUSIONS Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.

scholarly journals Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

10.2196/21978 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21978
Author(s):  
Sakun Boon-Itt ◽  
Yukolpat Skunkan
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Public Perception ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Public Awareness ◽  
Good Communication ◽  
Three Stages ◽  
Twitter Users

Background COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.

Customers' experience of purchasing event tickets: mining online reviews based on topic modeling and sentiment analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krzysztof Celuch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Third Party ◽  
Content Type

PurposeIn search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.Design/methodology/approachThis is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.FindingsThe findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.Originality/valueDrawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.

scholarly journals COP-eration for global food security

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2814 ◽  
Author(s):  
Erick de la Barrera
Keyword(s):  
Food Production ◽  
Biological Diversity ◽  
Convention On Biological Diversity ◽  
World Food ◽  
Global Food Security ◽  
The World ◽  
Aichi Biodiversity Targets ◽  
Cultural Connections ◽  
Urban Civilization ◽  
Global Food

Mexico is hosting the 13th Conference of the Parts (COP-13) on the Convention on Biological Diversity. Participants will have another opportunity to "integrate biodiversity for wellbeing." Considering that food production is a major driver for the loss of biological diversity, despite the fact that ample genetic reservoirs are crucial for the persistence of agriculture in a changing world, food can be a conduit for bringing biodiversity into people's minds and government agendas. If this generation is going to "live in harmony with nature," as the Aichi Biodiversity Targets indicate, such an integration needs to be developed between the agricultural and environmental sectors throughout the world, especially as an increasingly urban civilization severs its cultural connections to food origin.

scholarly journals Wikipedia, a Tool for Knowledge Dissemination on Invasive Species

2019 ◽  
Vol 3 ◽  
Author(s):  
Quentin Groom ◽  
Vanessa Lozano ◽  
Sofie Meeus ◽  
Carlos Olmedo Castellanos
Keyword(s):  
Invasive Species ◽  
General Public ◽  
Information Needs ◽  
Biological Diversity ◽  
Public Awareness ◽  
Current Status ◽  
Convention On Biological Diversity ◽  
English Edition ◽  
The Public ◽  
Global Issue

One of the most important issues for controlling the spread of invasive species is public awareness (Dehnen-Schmutz et al. 2018). Passively, the public are responsible for the spread of invasive species, for example by moving dirty boats from one water body to another. But they also actively spread invasive species by releasing them in the wild. The spread of some invasive species could be slowed or even halted if the general public were more aware of the risks. Furthermore, this is a global issue, not restricted to one country or language, and hence needs to be tackled at an international level. The Convention on Biological Diversity has a specific target to identify pathways of introduction and manage invasive species (Convention on Biological Diversity 2014). As part of this awareness-raising, the public needs information on how to identify invasive species, as well as how to know their regulatory status, distribution and transmission. They also need to be aware of how invasive species impact biodiversity, ecosystem services and health. This information needs to be up-to-date, reliable and unbiased, but also supported by evidence. Many information platforms exist on invasive species and new information is being generated all the time on this dynamic issue. However, pre-eminent among information sources on the internet is Wikipedia. Consistently ranked in the top ten of most visited websites, with more than 15 billion page views a month across 300 language editions, it is the go-to website on many subjects (Wikimedia 2019). Invasive species such as the zebra mussel and water hyacinth receive over 500 daily page views, just on the English edition alone. Wikipedia is arguably one of the largest citizen science projects, containing information on every area of science and connecting, through its citations, scientific literature with the general public. Wikipedia has been criticized about many aspects of its content and editorship, including its quality and neutrality (Kumar et al. 2016, Wagner et al. 2016, Hargittai and Shaw 2014). Nevertheless, its dominance in the provision of information cannot be ignored and addressing Wikipedia's problems by direct engagement might be more productive than ignoring it. Increasingly, different disciplines are engaging with Wikipedia for communication, recognizing that it can be a productive communication channel (Murray 2018). Invasive species are a global problem and tackling them is a global issue. We have been investigating the current status of invasive species information on Wikipedia, whether it contains relevant information on impacts and control, and whether the information is consistent across different language versions. We will present what we have found and make recommendations on what to improve and how we can engage with Wikipedia.

scholarly journals Study of the Yahoo-yahoo Hash-tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms

2021 ◽  
Author(s):  
Adebayo Abayomi-Alli ◽  
Olusola Abayomi-Alli ◽  
Sanjay Misra ◽  
Luis Fernandez-Sanz
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Latent Semantic Indexing ◽  
Workflow Engine ◽  
Semantic Indexing ◽  
Short Period ◽  
Mining Algorithms ◽  
Insight Into

Abstract BackgroundSocial media opinion has become a medium to quickly access large, valuable, and rich details of information on any subject matter within a short period. Twitter being a social microblog site, generate over 330 million tweets monthly across different countries. Analyzing trending topics on Twitter presents opportunities to extract meaningful insight into different opinions on various issues.AimThis study aims to gain insights into the trending yahoo-yahoo topic on Twitter using content analysis of selected historical tweets.MethodologyThe widgets and workflow engine in the Orange Data mining toolbox were employed for all the text mining tasks. 5500 tweets were collected from Twitter using the 'yahoo yahoo' hashtag. The corpus was pre-processed using a pre-trained tweet tokenizer, Valence Aware Dictionary for Sentiment Reasoning (VADER) was used for the sentiment and opinion mining, Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) was used for topic modeling. In contrast, Multidimensional scaling (MDS) was used to visualize the modeled topics. ResultsResults showed that "yahoo" appeared in the corpus 9555 times, 175 unique tweets were returned after duplicate removal. Contrary to expectation, Spain had the highest number of participants tweeting on the 'yahoo yahoo' topic within the period. The result of Vader sentiment analysis returned 35.85%, 24.53%, 15.09%, and 24.53%, negative, neutral, no-zone, and positive sentiment tweets, respectively. The word yahoo was highly representative of the LDA topics 1, 3, 4, 6, and LSI topic 1.ConclusionIt can be concluded that emojis are even more representative of the sentiments in tweets faster than the textual contents. Also, despite popular belief, a significant number of youths regard cybercrime as a detriment to society.

scholarly journals Indigenous Protected and Conserved Areas (IPCAs), Aichi Target 11 and Canada’s Pathway to Target 1: Focusing Conservation on Reconciliation

Land ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Melanie Zurba ◽  
Karen Beazley ◽  
Emilie English ◽  
Johanna Buchmann-Duck
Keyword(s):  
Protected Areas ◽  
Indigenous Peoples ◽  
Biological Diversity ◽  
Indigenous Communities ◽  
Convention On Biological Diversity ◽  
Colonial History ◽  
The Past ◽  
Nation States ◽  
Different Types ◽  
Aichi Biodiversity Targets

This article provides analysis of the issues relating to movement towards new models for Indigenous-led conservation in light of Canada’s initiatives for greater protected areas representation through Target 1. We provide a background on Canada’s Pathway to Target 1, which is based on Target 11 from the Aichi Biodiversity Targets set forth by the Convention on Biological Diversity (CBD). We contemplate the past, present and future of colonization and reconciliation in Canada, and consider the influence of international declarations, programs and initiatives on the potential for the formation of Indigenous Protected and Conserved Areas (IPCAs). We then provide an analysis of “wicked problems” that Indigenous communities, governments, and other stakeholders in protected areas will need to navigate towards implementing the IPCA approach in Canada. We outline the different types of Indigenous involvement in protected areas and how they potentially fit within the principles for the development of IPCAs. We then turn our discussion to the need to refocus conservation on reconciliation by restoring nation-to-nation relationships and relationships between the land and peoples. The lessons we draw have potential parallels for other nation states, particularly those signatory to the CBD and with a colonial history, aiming for biodiversity conservation and reconciliation with Indigenous peoples through IPCAs.

Sign in / Sign up

Export Citation Format

Share Document