This study examines how listeners’ emotional perspectives are influenced by the tone, lyrics, and themes of highly melancholic songs, focusing on their emotional engagement, interpretation of lyrics and melodies, and the expression of nuanced emotional responses in their comments. Additionally, we explore the alignment between listener feedback and the lyrical themes portrayed in these songs. Our analysis reveals that listener responses encompass a broad spectrum of emotional engagement, including appreciation for the heart-wrenching qualities of the music, the evocation of memories of loved ones, reflections on personal and collective struggles, resilience in the face of adversity, and empathy or compassion for others. These emotional reactions are consistently present across most of the acclaimed sad songs examined in this study, although their intensity and prevalence vary depending on specific factors such as the lyrics, tone, and other musical attributes. Overall, this study provides valuable insights into human emotional resonance with sad music, offering implications for fields such as psychology, emotion research, music therapy, and mental health support.

With illegal immigration remaining a contentious issue in the United States, understanding public perception—particularly as expressed on social media—is vital for informed policymaking and advocacy. This study investigates the discourse surrounding illegal immigration in the U.S. through both quantitative and qualitative analyses of data from Reddit. We identify key themes and perspectives on four critical aspects: (i) U.S. policy toward illegal immigration, (ii) strategies for mitigating illegal immigration, (iii) issues arising from illegal immigration, and (iv) hostile rhetoric targeting undocumented immigrants. Additionally, we conduct emotion analysis to ascertain prevalent emotional responses within the discourse. Our findings reveal both challenges and perceived benefits associated with lenient border policies, including difficulties in monitoring extensive border areas and the exploitation of low-wage labor. We observe a wide range of suggested measures to reduce illegal migration, including stringent actions against employers of undocumented workers, proposals to abolish birthright citizenship, and calls for increased legal migration. Emotion analysis indicates significant levels of anger and disgust expressed in the comments. Furthermore, we document instances of anti-immigrant rhetoric, some of which are inhumane and include calls for violence against undocumented individuals. This research provides valuable insights into societal attitudes toward illegal immigration, informing policymakers and advocacy groups in their decision-making.

The demographic knowledge facilitates a fine-grained interpretation of the user-generated review text and enables better decision-making. In this study, we aim to com-prehend how various attributes of non-native English text vary across demographically distinct groups. We introduce a non-native English corpus of around 1150 reviews representing four demographically diverse country-specific groups: Finland, Kenya, Bangladesh, and China. The reviews differ in various contexts, including geography, native language family, race and culture, and English proficiency levels of the reviewers. We then perform stylometric and semantic analysis on these distinct sets of reviews to unveil how the linguistic characteristics differ across the demography. The investigation reveals that stylometric features are mostly similar across the reviews of various groups; nevertheless, dissimilarities are observed in attributes, such as review length, presence of articles, or prepositions. We employ classical machine learning (ML) algorithms and transformer-based fine-tuned language models for categorizing the reviews into distinct demographic groups. We observe that semantic features yield slightly better efficacy than syntactic features for distinguishing the demography-specific reviews.

Due to the disastrous global impact of the recent Russia-Ukraine conflict, people all over the world are getting concerned- expressing their opinions and feelings toward it on social media such as Twitter and Facebook. To comprehend people’s perceptions and reactions, in this study, we analyze around 80000 demographically diverse tweets representing Twitter users of 7 countries across four continents. We first perform sentiment analysis to reveal how sentiments are expressed across demographically diverse tweets. We notice a similar pattern in the conveyed opinions and sentiments across the groups, constituting a high number of negative tweets citing the disastrous consequence of the war and criticism of the aggression. In addition, we investigate the presence of highly offensive words and phrases in various demographic groups. Finally, we employ two topic modeling approaches, LDA and transformer-based BERTopic, to find the most prevalent and discussed topics across users of demographic groups. We witness the presence of a diverse set of topics in the tweets of various groups, with dominant themes conveying support for Ukraine, the destructive impact of the war, and the resolution of this conflict. Our analysis reveals the feelings, thoughts, and sentiments of demographically diverse Twitter users regarding this conflict and provides insights for understanding people’s perceptions across the globe.