Jul 2026· İletişim Kuram ve Araştırma Dergisi· 0 citations· 56 references
TL;DR
While sentiment analysis has matured from an experimental technique to a core methodology in communication, it risks methodological stagnation due to data source limitations, and future research is suggested to focus on multimodal analysis and diverse digital platforms to overcome these constraints.
Abstract
This study aims to map the intellectual structure, chronological evolution, and social networks of sentiment analysis research within the field of communication. As digital text data grows, text-as-data methods have become central to communication studies; however, a comprehensive bibliometric evaluation of this domain remains limited. The research analyzes 298 articles indexed in the Web of Science (SSCI and ESCI) between 2011 and 2025 using the PRISMA protocol. A hybrid methodological approach was adopted, combining computational bibliometric analysis via the R-bibliometrix package with manual quantitative content analysis. The findings reveal that sentiment analysis related studies experienced a latency period between 2011 and 2015, followed by exponential growth, particularly after 2020. Thematically, the literature clusters around political communication, journalism, and crisis communication, with a heavy reliance on social media data. While the United States leads in total scientific production, European countries demonstrate higher centrality in international collaboration networks. A critical finding is the field’s platform dependency, as 32% of the studies exclusively rely on Twitter (X) data. The study concludes that while sentiment analysis has matured from an experimental technique to a core methodology in communication, it risks methodological stagnation due to data source limitations. Future research is suggested to focus on multimodal analysis and diverse digital platforms to overcome these constraints.
This article, which presents a bibliometric analysis of discourse analysis research in Türkiye, systematically evaluates nearly 50 years of production in the field using the Scopus database. The findings of the research indicate that discourse studies in Türkiye are predominantly concentrated in the social sciences and humanities. The most prominent thematic concentrations are on political discourse, nationalism, populism, gender, and European Union debates. While Critical Discourse Analysis stands out as the dominant methodological approach, innovative techniques such as multimodal analysis or corpus-based methods appear to be insufficient. An examination of language preferences reveals that English is overwhelmingly preferred for global visibility, while Turkish remains an important local production language. Institutional performance analyses reveal that Bilkent, Koç, and Boğaziçi universities hold leading positions in terms of both publication volume and citation impact. An examination of collaborative networks among authors reveals that individuals such as Deniz Zeyrek and Bedrettin Yazan play a central role. However, it is also true that a significant portion of the 2,883 authors remain outside these collaborative networks. International collaborations and citation rates demonstrate that the integration of researchers in Türkiye with the global academy is becoming increasingly stronger, but this interaction can be further enhanced. The study also points to significant gaps in the existing literature. The limited number of discourse studies in the fields of Science, Technology, Engineering, and Mathematics, the inadequate research in contemporary contexts such as digital platforms and artificial intelligence, and the limited methodological diversity are among the most prominent shortcomings.
This study aims to map the intellectual and conceptual landscape of the B Corp movement within the scientific literature through a quantitative bibliometric analysis. Utilising the Web of Science (WoS) Core Collection database, the research analyses 108 peer-reviewed articles published between 2007 and 2025. Data visualisation and network analysis, including co-authorship and keyword co-occurrence, were performed using the VOSviewer software. The results show that the number of publications has been growing exponentially since 2011, and the majority of the articles are in the business and management field. A total of 282 keywords were evaluated, and “B Corp” (total link strength: 315) and “Corporate Social Responsibility” (total link strength: 92) were identified as central conceptual pillars. The most cited influential work is by Julie Battilana and Alnoor Ebrahim, with 728 citations. Geographic concentration is still strong in the USA and Europe, but research on emerging economies, such as Türkiye, where there are presently 15 certified B Corps, is critically lacking. The results show a conceptual move from general corporate responsibility to specific issues of 'social impact' and 'employee ownership'. The paper concludes with a research agenda for future research that can help bridge the gap between global norms and local institutional contexts, particularly for researchers in Türkiye.
Şafak Altay· Trends in Business and Econo...· 0 citations
This study aims to systematically reveal the intellectual structure, collaboration networks, and thematic orientations of the field by examining the New Economic Sociology (NES) literature indexed in the Web of Science Core Collection database using bibliometric methods. A search conducted on October 19, 2025, using the keyword ‘new economic sociology’ yielded 238 publications covering the period 1987-2025. Analyses reveal that NES, developed under the influence of founding figures such as Granovetter, Swedberg, and Beckert, has evolved into a multi-layered field of research that examines economic action within social networks, institutions, cultural frameworks, and uncertainty dynamics. This study analysed co-authorship, citation networks, country and institutional contributions, keyword formations, bibliographic matches of documents and authors, and common citation structures using VOSviewer. The findings show that NES has a strong theoretical core and that the themes of embeddedness, network relations, and institutional coordination remain dominant. Sustainable Development Goals analyses reveal that the NES intersects meaningfully with goals such as innovation, economic growth, reducing inequalities, and sustainable cities. This study aims to provide a comprehensive assessment of the current state of the field and future research directions by making the structural characteristics of the NES literature visible.
Özcan Garan· Sosyal Siyaset Konferansları...· 0 citations
This study aims to systematically map the evolution, influence structure and thematic organization of information overload research and to uncover latent research directions not directly observable from bibliometric metadata, with particular attention to the socio-technical dynamics of the digital workplace.
A corpus of 1,081 English-language research articles, review articles and conference papers indexed in the Web of Science Core Collection and Scopus and published between 2000 and March 2026 was analyzed. The study combines descriptive bibliometric indicators, keyword co-occurrence network analysis and Latent Dirichlet Allocation (LDA) topic modeling to examine the structural and semantic development of the field.
Research on information overload has expanded rapidly since 2018, reaching a peak in 2025. The literature exhibits a pronounced socio-technical structure linking algorithmic filtering, information systems, knowledge management, managerial decision-making and employee well-being. Topic modeling identifies eight major themes, with recommender systems and social media in digital work environments emerging as the most prominent and fastest-growing streams.
This article advances information overload research by integrating bibliometric science mapping and machine learning within a unified analytical framework. It shows how the field has evolved from cognitive and communication-centered perspectives toward a broader socio-technical understanding of digital work and offers a structured agenda for cumulative theory development.
Jan Schmutzer, P. Vrabcová· Journal of Documentation· 0 citations
Arabic Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large language models (LLMs). Despite this growth, a comprehensive quantitative meta-analysis remains absent. This study presents a bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026, sourced from five platforms (arXiv, ACL Anthology, Semantic Scholar, Crossref, OpenAlex) plus an additional targeted OpenAlex subset. We employ BERTopic for topic modeling, regression analysis, social network analysis, and geographic mapping. Our findings show a significant publication surge after 2020, driven by transformer models and LLMs. Topic modeling identifies 19 themes, the largest centered on text, speech, translation, and recognition (2,942 papers). Citation analysis reveals a positive correlation between paper age and citations (r = 0.245, p<0.001); regression (R^2 = 0.105) shows that indexing in OpenAlex or Semantic Scholar and institutional affiliation are associated with higher citations. Saudi Arabia, the United States, and Egypt lead in research output. A task-dialect gap matrix identifies understudied areas, including summarization for Maghrebi, Iraqi, and Sudanese dialects. The largest topic has the highest H-index (90), followed by sentiment analysis (57). Our quantitative approach complements existing qualitative surveys and offers recommendations to prioritize under-resourced dialects and develop culturally aligned benchmarks.
: In the digital era, social media platforms have become vital arenas for brand image co-construction, yet computational sentiment analysis often lacks the linguistic depth to interpret the nuanced mechanisms behind emotional expression, particularly for time-honored brands. This study bridges this gap by integrating Appraisal Theory with quantitative text mining to analyze 4,176 user comments about the heritage catering brand "Tao Tao Ju" on Xiaohongshu. Employing ROST CM6 and ROST EA for sentiment polarity, word frequency, and semantic network analysis, alongside a qualitative manual coding of Attitude, Graduation, and Engagement resources, the research yields several key findings. Results indicate that overall sentiment is predominantly positive (68.1%), centered on Appreciation of culinary offerings and nostalgic ambiance, but a significant cluster of negative Judgment (23.6%) targets service inefficiency. The semantic network reveals a structural decoupling between product and service evaluations. Theoretically, this study demonstrates the feasibility of an interdisciplinary "data-to-discourse" framework, while practically, it provides a precise diagnostic tool and strategic content suggestions for the digital transformation of heritage brands.
Jinhan Liu, Liang Ling· Media and Communication Rese...· 0 citations