There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

Sentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space.

In the process of neoliberal transformation in Turkey, what differentiated the 2000s from the previous two decades were the block sale privatizations of large-scale state enterprises such as PETKİM, Türk Telekom, TÜPRAŞ, and ERDEMİR. These block sales, the conditions of which were shaped by political struggles at different levels, were also constitutive political and ideological moments per se, helping to reproduce a particular perception of social reality at the expense of others. This paper will overview and critically problematize the privatization processes of these four enterprises, all completed under the successive AKP governments in power since 2002. By focusing on the apparently technical and economic aspects of the block-sale processes, such as valuation, efficiency enhancement and marketing, the paper calls into question the increased concerns over their transparency, and wonders whether such concerns can be understood as attempts to mask the substantially corrupt nature of capitalist relations of production, which inescapably makes itself felt during these processes.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

How do centralized governments mitigate the political cost of severe financial crises? The economic voting scholarship has established that the clarity of responsibility, i.e., government accountability for economic conditions to the mass public, is a necessity for electoral reward or punishment for economic performance. On the one hand, political centralization, which reduces the number of veto players, may increase the visibility of the role of the executive in policy success or failure. On the other hand, it allows an uncontested blame avoidance discourse, especially when accompanied with democratic backsliding. Furthermore, the recent backlash against globalization has enabled blame shifting to international actors in many countries. Against this theoretical framework, we comparatively analyze the responsibility attribution discourses for the 1994, 2001, and 2018-2022 financial crises in the statements of incumbent presidents, ministers, and parliament members of Turkey. We find that while blame avoidance discursive strategies have been attempted in all three cases, the responsibility attribution for the 1994 and 2001 crises mostly targeted the executive. In contrast, for the ongoing crisis, the responsibility discourse is dominated with blaming international political economy factors, creating ambiguity, and targeting domestic non-governmental actors.

Investment arbitrations should not happen too often, because they are costly processes for both parties. Yet they regularly happen. Why? We investigate the hypothesis that investment arbitrations are used as a means of last resort, after dissuasion has failed, and that dissuasion is most likely to fail in situations where significant political risk materializes. Investment arbitration should thus tend to target countries in which high-political risk has materialized. In order to test this hypothesis, we focus in this article on two drivers of political risk: bad governance and economic crises. We test various links between these two drivers of risk and arbitration claims. We use an original dataset that includes investment claims filed under the rules of all arbitration institutions as well as ad hoc arbitrations. We find that bad governance, understood as corruption and lack of rule of law (using the WGI Corruption and WGI Rule of Law indexes), has a statistically significant relation with investment arbitration claims, but economic crises do not.