| 1. |
- Rosenberg, Emelie, et al.
(författare)
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Sentiment analysis on Twitter data towards climate action
- 2023
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Ingår i: Results in Engineering (RINENG). - : Elsevier BV. - 2590-1230. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the task of uncovering the sentiment of Twitter users concerning climate-related issues. This is done by applying modern natural-language-processing (NLP) methods, i.e. VADER, TextBlob, and BERT, to estimate the sentiment of a gathered dataset based on climate-change keywords. A transfer-learning-based model applied to a pre-trained BERT model for embedding and tokenizing with logistic regression for sentiment classification outperformed the rule-based methods VADER and TextBlob; based on our analysis, the proposed approach led to the highest accuracy: 69%. The collected data contained significant noise, especially from the keyword 'energy'. Consequently, using more specific keywords would improve the results. The use of other methods, like BERTweet, would also increase the accuracy of the model. The overall sentiment in the analyzed data was positive. The distribution of the positive, neutral, and negative sentiments was very similar in the different SDGs.
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| 2. |
- Sanchez-Roncero, A., et al.
(författare)
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ASDG - An AI-based framework for automatic classification of impact on the SDGs
- 2022
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 119-123
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Konferensbidrag (refereegranskat)abstract
- Achieving the Sustainable Development Goals of the United Nations is the primary goal of the 2030 Agenda. A critical step towards that objective is identifying if the scientific production is going in this way. Funders must do a manual recognition, impacting accuracy, scalability, and objectiveness. For this reason, we propose in this work an AI-based model for the automatic classification of scientific papers based on their impacts on the SDGs. The training database consists of manually extracted texts from the UN page. After preprocessing these texts, we train three models: NMF, LDA, and Top2Vec. The output of these models is the probability of a paper being associated with each SDG. We then combine their scores by implementing a voting function to take advantage of their inherently different mathematical nature. To validate this methodology, we use the database provided by Vinuesa et al., Nature Communications 11, with more than 150 papers labeled with at least 1 SDG. Using only the abstracts, we correctly identify a of the SDGs presented in a paper, while a better is obtained when fetching the complete paper information. Moreover, we find that the other identified SDGs which were not labeled are also related to the text contents. We recognize that more training files are required for the other cases since they are based on more complex human reasoning. We open-source these databases and trained models to enable future investigation in this field and allow public institutions to use this tool.
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| 3. |
- Sanchez-Roncero, Alejandro, et al.
(författare)
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The Sustainable Development Goals and Aerospace Engineering : A critical note through Artificial Intelligence
- 2023
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Ingår i: Results in Engineering (RINENG). - : Elsevier BV. - 2590-1230. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use Artificial-Intelligence-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities), and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with apparent differences in some SDGs. Finally, the number of abstracts the code cannot identify decreases with time, possibly showing the scientific community's awareness of SDG.
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