topic modelling
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2022 ◽  
Vol 34 (3) ◽  
pp. 1-18
Author(s):  
Fang Qiao ◽  
Jago Williams

With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques—topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and Covid-19; global warming’s relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public’s principal concerns and their feelings about global warming on Twitter.


2022 ◽  
Vol 16 (1) ◽  
pp. 101224
Author(s):  
Omar Ballester ◽  
Orion Penner
Keyword(s):  

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Liam Wright ◽  
Elise Paul ◽  
Andrew Steptoe ◽  
Daisy Fancourt

Abstract Background During the COVID-19 pandemic, the UK government implemented a series of guidelines, rules, and restrictions to change citizens’ behaviour to tackle the spread of the virus, such as the promotion of face masks and the imposition of lockdown stay-at-home orders. The success of such measures requires active co-operation on the part of citizens, but compliance was not complete. Detailed research is required on the factors that aided or hindered compliance with these measures. Methods To understand the facilitators and barriers to compliance with COVID-19 guidelines, we used structural topic modelling, a text mining technique, to extract themes from over 26,000 free-text survey responses from 17,500 UK adults, collected between 17 November and 23 December 2020. Results The main factors facilitating compliance were desires to reduce risk to oneself and one’s family and friends and to, a lesser extent, the general public. Also of importance were a desire to return to normality, the availability of activities and technological means to contact family and friends, and the ability to work from home. Identified barriers were difficulties maintaining social distancing in public (due to the actions of other people or environmental constraints), the need to provide or receive support from family and friends, social isolation, missing loved ones, and mental health impacts, perceiving the risks as low, social pressure to not comply, and difficulties understanding and keep abreast of changing rules. Several of the barriers and facilitators raised were related to participant characteristics. Notably, women were more likely to discuss needing to provide or receive mental health support from friends and family. Conclusion The results demonstrated an array of factors contributed to compliance with guidelines. Of particular policy importance, the results suggest that government communication that emphasizes the potential risks of the virus and provides simple, consistent guidance on how to reduce the spread of the virus would improve compliance with preventive behaviours as COVID-19 continues and for future pandemics.


Author(s):  
Roman Egger ◽  
Angela Pagiri ◽  
Barbara Prodinger ◽  
Ruihong Liu ◽  
Fabian Wettinger

AbstractThe needs of travellers vary across cultures. When it comes to culinary aspects, there is a strong connection between gastronomy and culture. To optimise service offerings, investigation of the essential aspects of dining experiences in relation to cultural backgrounds is of great importance. In the age of digitalisation, tourists share their dining experiences throughout their multiphasic travel journey via online platforms. By considering nine distinct cultural backgrounds, this research aims to investigate tourist experiences based on TripAdvisor restaurant reviews through topic modelling, using the city of Salzburg as its study context. Depending on one’s cultural circumstances, the findings demonstrate that the most important aspects include staff, food-menu items, value for money, restaurant physical appearance, food authenticity, overall service, menu offers, food quality, atmosphere, and recommendations. This study advances the state-of-the-art knowledge of societal culture as a variable in the target market analysis of restaurant customers. Findings allow restaurant owners, other tourism service providers, and destination management organisations to analyse and adapt their service offerings and strategies accordingly.


Author(s):  
Artjoms Šeļa ◽  
Boris Orekhov ◽  
Roman Leibov
Keyword(s):  

A dolgozat egy már meglévő, „a versmérték jelentésmezőjeként” ismert költészetelmélet formalizálását kísérli meg, amely elmélet azt állítja, hogy a modern líra különböző metrikai formái bizonyos jelentésbeli asszociációkat halmoznak fel és őriznek meg. Az LDA témamodellező (topic modelling) algoritmussal vizsgáltuk az orosz költészet tág korpuszát (1750–1950), hogy ezáltal minden egyes verset egy tématérben, a versmértékeket pedig a témák valószínűségének eloszlása szerint reprezentáljunk. Nem felügyelt osztályozást és kiterjedt mintavételt alkalmazva megmutatjuk, hogy a verselési formákon belül és között erős a forma és a jelentés kapcsolata: ugyanahhoz a versmértékhez tartozó két minta sokszor nagyon is hasonlóként tűnik fel, és ugyanannak a családnak két verselési formája legtöbbször szintén egy klaszterbe kerül. Ez a kapcsolat akkor is kimutatható, ha a korpusz kronológiai szempontból ellenőrzött, és nem következménye a populáció méretének. Amellett érvelünk, hogy hasonló megközelítést nyelvek és költészetihagyományok szemantikai mezőinek összehasonlításakor is alkalmazni lehet, amelynek révén az irodalomtörténet legalapvetőbb kérdéseire adhatók releváns válaszok.


2021 ◽  
Vol 5 (4) ◽  
pp. 7-22
Author(s):  
MARIUSZ BARANOWSKI ◽  
PIOTR CICHOCKI

The changing social reality, which is increasingly digitally networked, requires new research methods capable of analysing large bodies of data (including textual data). This development poses a challenge for sociology, whose ambition is primarily to describe and explain social reality. As traditional sociological research methods focus on analysing relatively small data, the existential challenge of today involves the need to embrace new methods and techniques, which enable valuable insights into big volumes of data at speed. One such emerging area of investigation involves the application of Natural Language Processing and Machine-Learning to text mining, which allows for swift analyses of vast bodies of textual content. The paper’s main aim is to probe whether such a novel approach, namely, topic modelling based on Latent Dirichlet Allocation (LDA) algorithm, can find meaningful applications within sociology and whether its adaptation makes sociology perform its tasks better. In order to outline the context of the applicability of LDA in the social sciences and humanities, an analysis of abstracts of articles published in journals indexed in Elsevier’s Scopus database on topic modelling was conducted. This study, based on 1,149 abstracts, showed not only the diversity of topics undertaken by researchers but helped to answer the question of whether sociology using topic modelling is “good” sociology in the sense that it provides opportunities for exploration of topic areas and data that would not otherwise be undertaken.


2021 ◽  
Author(s):  
Totok Wahyu Wibowo ◽  
Sigit Heru Murti Budi Santosa ◽  
Bowo Susilo ◽  
Taufik Hery Purwanto

Author(s):  
Dinusha Vatsalan ◽  
Raghav Bhaskar ◽  
Aris Gkoulalas-Divanis ◽  
Dimitrios Karapiperis

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