Word Association Analysis Research Articles

A longitudinal qualitative study on Iranian university lecturers' identity reconstruction during the COVID-19 pandemic through text-mining: a dynamic approach

This research addressed the critical gap in understanding the procedure of professional identity reconstruction among university lecturers in Iran during the pandemic. Over the course of two academic semesters, this study utilized a dynamic theoretical framework to investigate the intricacies of how 41 Iranian university English teachers reconstructed their professional identities. Data were collected through a dynamic methodological framework, comprising: semi-structured interviews conducted monthly, focus group interviews held every 3 months, and participants' reflective journal entries collected every 2 weeks. Text mining techniques and content analysis, through KH Coder 3.0, were employed to explore the nuanced aspects of teachers' identity reconstruction, focusing on contextual elements, agency, and pedagogical practices. The use of KH Coder 3.0 software for word co-occurrence and word association analysis revealed that university lecturers reconstructed their professional identities in response to the challenges posed by two main factors: (1) adapting to new educational contexts and (2) overcoming difficulties in teacher–student communication caused by the abrupt shift from traditional in-person education to online schooling. Through their practical experiences, educators emerged as proactive problem solvers, receptive adapters, and confident professionals. These findings carry implications for educational institutions and teacher educators, emphasizing the importance of prioritizing pedagogical systems that encourage teachers to excel in hybrid teaching contexts, fostering proficiency in online communication and virtual teaching methods.

Policy Suggestions through Social Perception Analysis of Defense YouTube Channels

YouTube is a platform that delivers video content on diverse topics and enables communication, making it the most utilized service for conveying knowledge and exchanging information. The Korean Army is also proactively operating official Defense YouTube channels related to the military and producing videos to promote a positive perception and instill faith in the military among the general public and soldiers. However, there is a lack of research on the current status of the content and societal perceptions of defense-related channels. To understand societal awareness, we qualitatively analyzed video content and comments. In addition, we used big data analysis methods such as keyword derivation, term frequency analysis, word cloud visualization, word association analysis, and sentiment analysis of comments based on a Korean sentiment dictionary. These analyses identify current defense channel strategies and society's awareness of these strategies and provide a foundation for improving and perfecting future video creation and operation processes.

Free word association analysis of German laypeople's perception of biodiversity and its loss.

Due to the dramatic biodiversity crisis, it is crucial to understand how people perceive biodiversity. Knowledge of how thoughts are organized around this concept can identify which ideas are best to focus on biodiversity conservation information campaigns. The primary aim of the present study was to identify social representations of the German public regarding the concept of biodiversity and its loss using a free word association test. Furthermore, unique association networks were analyzed. For this purpose, data collection was performed in September 2021 in Germany using an online questionnaire to assess participants' associations with the prompt "biodiversity" (n = 131) and "biodiversity loss" (n = 130). Additionally, we used the social network software Gephi to create biodiversity (loss) association networks. The five most commonly mentioned associations for biodiversity were "animal," "plant," "nature," "human," and "flower." For biodiversity loss, the five most commonly mentioned associations were "species extinction," "climate change," "plant," "insect," and "bee." Neither "land use change" nor "invasive species," as key drivers of biodiversity loss, were present in social representations of the German public. A difference was observed in the total number of mentioned associations between biodiversity and biodiversity loss. For both, the associations "plant" and "animal" were related. However, participants associated specific taxa only with animals, such as "insects" and "birds." For plants, no specific taxa were named. Based on the network analysis, the most commonly mentioned word pairs for biodiversity and biodiversity loss were "plant - animal" and "species loss - climate change," respectively. Based on our statistical network analysis, these associations were identified as the most central associations with the greatest influence in the network. Thus, they had the most connections and the function of predicting the flow in the network. In sum, the public's multifaceted views on biodiversity and its loss, as well as the aforementioned central associations, hold great potential to be utilized more for the communication and education of biodiversity conservation. In addition, our findings contribute to the scientific community's understanding of social representations and perceptions of biodiversity and its loss.

Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling

Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.

Восприятие речи в цифровом контенте: сетевые конфликты в городских проектах

The article presents the results of the analysis on perception by network actors implementation of urban projects that affect the environment. An algorithm for adapting linguistic, psycholinguistic and sociolinguistic methods for interpreting Big Data in real time is proposed. The study has enabled identifying several conflict situation markers and rating social tension compilation. The transdisciplinary approach has been applied with the use of neural network technologies, text analysis, analysis of word associations, content analysis, sentiment analysis. The material for the study was the data of social networks, microblogs, blogs, instant messengers, video hosting, video materials, forums and reviews on the construction of metro stations in Moscow. Analysis of the data showed the ambivalent attitude of Muscovites to the construction of new metro facilities. A positive reaction, due to the need to develop the transport structure of the city, improve the transport situation, is confronted with the protest of residents who put forward a number of claims. Verbal data examination has made it possible to reveal the rating of social tension around new metro facilities. The selection and analysis of the semantic and associative network with the use of neural network technologies contributed to clarifying and expanding the linguistic paradigm in speech perception of digital content. The results of the study can be used in both text and network analysis.

The Body after Cancer: A Qualitative Study on Breast Cancer Survivors' Body Representation.

Objective: The relationship with the body is a relevant issue for breast cancer survivors. Oncological treatments damage their bodies due to scars, weight gain, and other side effects. Starting from the efficacy of psychological interventions for breast cancer survivors, a tailored psychological support program was provided to promote overall well-being after illness dealing with bodily signals and related emotions and thoughts. This study presents changes in the description of the relationship with their bodies as well as participants’ emotions and thoughts before and after a psychological intervention. Methods and Measures: Eighteen women answered questions related to their bodies before and after the psychological intervention. Results were analyzed in accordance with the procedure of the Word Association Analysis through the T-Lab software and the Qualitative Thematic Analysis. Results: Participants reported a great awareness of their bodies and the desire to take care of them daily. In particular, the body is now perceived as a helper to sustain breast cancer survivors in their everyday activities. Conclusion: The words and the themes that characterized the participants’ reports highlighted the impact of cancer diagnosis and oncological therapies on breast cancer survivors. The participation in the psychological intervention focused on self-compassion towards their body helps women to create an improved body perception.

Free word association analysis of Germans' attitudes toward insects

AbstractIn this paper, the results of two free word association tests were analyzed to gain insight into Germans' attitudes toward insects. We used a novel approach in the form of an association network with the help of the software “Gephi” that was originally developed for social network analysis. The influence of attitude on the willingness to donate (WTD) and actual donation to an insect conservation project was investigated as well. Data collection was conducted via an online questionnaire (n = 515; Mage = 49.36, SD = 16.73; female = 50.1%). For the first test, participants listed three associations for the prompt “insect.” The associations were assigned to the three components of attitude: affective, cognitive, and behavioral. For the second test, participants named insects they pictured when thinking about “insects.” The results were taxonomically classified. The WTD was assessed with a Likert‐type scale and an actual donation could be made at the end of the online questionnaire. “Bee” was the most frequently named association, followed by “useful,” “nature,” “pollination,” and “pesky.” “Pesky” was most often named with “useful,” indicating that being aware of insects' usefulness is not enough to supersede negative associations. In the second test, only 6% of the associations were on the species level, which suggests little taxonomic knowledge about or interest in insects. Linear regression revealed that positive affective associations had a positive influence on the WTD and negative affective associations had a negative influence on the WTD, both with a small effect size. We advise educating people not only about the usefulness of insects but also fostering positive, personal encounters to increase positive affective associations and decrease negative ones.

Environmental digital conflicts: Spanish-, german-, and russian-speaking actors

Introduction. Almost all significant social communications are moving to virtual spaces. Thus, environmental conflicts play an increasingly important role in public life, as civic activity in solving environmental problems grows. The development of eco-territorial conflicts and requests for their social reactions lead to the emergence of digital conflict zones, sectors of the media space in which the current environmental agenda is discussed by a wide range of users. The analysis of conflicts in the digital environment is truly relevant and can be performed using neural network technologies. Methodology. Big data obtained from social media has become an important source of analysis of social processes, behavioral characteristics, speech perception, society's assessment of events and phenomena. The purpose of the work was to determine the specifics of perception in the media space of environmental conflicts in urban planning and construction. To analyze digital content, a multimodal approach was used along with neural network technologies, text analysis, sentiment analysis, analysis of word associations. The research data was collected using Brand Analytics and the corpus Sketch Engine. Content analysis was carried out using the multilingual technology of neural networks TextAnalyst 2.3. and visual analysis using the Tableau platform. Results and Conclusions. As a result of the study, common and different signs of the development of digital conflict zones related to environmental problems in the Spanish, German and Russian-speaking media space were identified.

Word association analysis of children with disabilities

Purpose: This study aimed to compare the types of response words for stimuli in children with language impairment and normal children through the word association task.Methods: For this study, 60 children with language impairment aged 9-11 years old and 60 normal children of the same language age as them were selected. The list of stimulus words for the word association task consisted of 25 nouns, 6 verbs and 6 adjectives.Results: The research results were as follows. First, it was found that the appearance rate of association words in the form and syntax among the intra-linguistic association was higher in the children with language impairment than in the normal children, and the appearance rate of association words in the meaning and pragmatics was lower than that of the normal children. Second, in children with language impairment, the appearance rate of verb response words was high in the noun stimulus words, and the noun response words appeared high in the verb and adjective stimulus words.Conclusions: In conclusion, compared to the normal children, the form and syntax-oriented syntagmatic associative responses were higher than the semantic-oriented paradigmatic associative responses, indicating that the stimulus and response words were used side by side in sentences.

Analysis of Research Trends Based on Text Mining in the Journal of Korean Society of Environmental Engineering

Objectives : The purpose of this study is to analyze research trends based on text mining technology from the published papers in the Journal of Korean Society of Environmental Engineering.Methods : From 2000 to 2019, a total of 2,743 published papers were analyzed using text mining techniques. Term frequency, TF-IDF for document classification, word association analysis were applied to find the characteristics of text data.Results and Discussion : When confirming the high appearance of the word in the published paper during 2000-2019, ‘adsorption’, ‘heavy metals’, ‘activated carbon’, ‘sediment’, ‘sewage sludge’ was found in order. It implies that large number of studies in the journal were focused on the water quality field mainly. TF-IDF analysis classified the studies into five groups; 1) drinking water treatment field, 2) water quality modeling field, 3) heavy metal adsorption field, 4) biological sewage treatment field, 5) environmental catalyst field. These results by TF-IDF show that a large proportion of studies were published in the field of water quality modeling and biological sewage treatment. When we analyzed the term frequency every five years, “adsorption” and “heavy metals” were the highly-frequency occurrence words from 2000 to 2009, but in the last 5 years, new words such as “fine dust”, “cesium”, and “ecological toxicity” were appeared. It seems that the research was reflected in the recent environmental issues.Conclusions : A lot of studies has been focused on the field of water quality but in recent years, new research topics are being studied related to atmosphere, toxicity, and radiation. Applying a more sophisticated and diverse text mining technique will be of great help to improve the environmental engineering research field.

Pandemic Dreams: Network Analysis of Dream Content During the COVID-19 Lockdown.

We used crowdsourcing (CS) to examine how COVID-19 lockdown affects the content of dreams and nightmares. The CS took place on the sixth week of the lockdown. Over the course of 1 week, 4,275 respondents (mean age 43, SD = 14 years) assessed their sleep, and 811 reported their dream content. Overall, respondents slept substantially more (54.2%) but reported an average increase of awakenings (28.6%) and nightmares (26%) from the pre-pandemic situation. We transcribed the content of the dreams into word lists and performed unsupervised computational network and cluster analysis of word associations, which suggested 33 dream clusters including 20 bad dream clusters, of which 55% were pandemic-specific (e.g., Disease Management, Disregard of Distancing, Elderly in Trouble). The dream-association networks were more accentuated for those who reported an increase in perceived stress. This CS survey on dream-association networks and pandemic stress introduces novel, collectively shared COVID-19 bad dream contents.

Physicochemical Properties and Consumer Acceptance of Bread Enriched with Alternative Proteins.

A projected global population growth by 2050 and climate change crises have led to increasing demand in edible protein sources; thus, scientific research and food industries are searching for alternatives. In this study, we investigated the incorporation of plant- and insect-based protein sources in wheat-based formulations. The Alveographic properties of dough and the effects on bread physicochemical and sensory characteristics were analysed. Including pea protein or insect powder improved the nutritional value, increasing protein content, but influenced the dough and bread properties. Pea protein significantly increased the dough extensibility (L), tenacity (P), and their ratio (P/L) in dough with insect blends and the control. Bread texture properties were significantly affected by the addition of pea and insect flour. Higher amounts of pea protein incorporation increased hardness values and showed a mean cell area lower than the control bread. Crust colour analysis showed significant differences concerning the control bread, while crumb colour was affected by the flour colour. Word association analysis showed insect bread was associated with an emotional dimension, wheat bread was linked with “tradition”, and pea bread was associated with “fruit and vegetable”.

Utilization of text mining as a big data analysis tool for food science and nutrition.

Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.

Family businesses, corporate social responsibility, and websites

PurposeThe purpose of this paper is to identify the distinctive elements of CSR communications that characterize the communications models of family businesses in the Italian wine industry, and to compare them with nonfamily businesses.Design/methodology/approachUsing a case study approach, a sample of large and medium companies practicing corporate social responsibility was identified. The content of their websites was examined using content analysis and text mining (correspondence analysis techniques and word association analysis using the T-Lab software).FindingsThe analysis indicates that the ownership structure nature makes a difference in the online CSR communications process. The cultural identity in both family and nonfamily businesses is founded on intangible factors such as tradition; however, being a family business is a fundamental driver in the online CSR communications process, no longer forming a bond among players in the wine industry, but rather linking with other wine family businesses.Research limitations/implicationsOne limitation of this work is the small size of the investigated sample. An added value it contributes is its focus on the Italian wine industry. The paper provides the essential elements that family and nonfamily wine businesses should consider in customizing their CSR communications with the brand’s specific details.Originality/valueThe authors highlighted the similarities and differences of family and nonfamily wine businesses in terms of their online CSR communications. The authors also observed how the family wine business identity, in its multidimensional construct, has common factors with what we call “familiness.” This research could establish a starting point for further work within this important sector.

Therapeutic Walks in Virtual Nature

Can the benefits of nature walks for psychological well-being and cognitive ability be replicated in a purely virtual environment? The private island of Natmoud was built in the virtual world of Second Life (SL) to see whether immersion in such an environment is possibly therapeutic (see Appendix). This exploratory study is based on a small convenience sample of SL residents who were invited by the author’s avatar to visit Natmoud island. The subjects were asked to comment in writing on their experience while there. Reading and analyzing these comments with TROPES semantic software allows us to identify the main factors contributing to a relaxing and comforting atmosphere with an analysis of word associations. The implicit hypothesis that a virtual representation of nature can be soothing is confirmed. We also find that wildlife—which is abundant on the island *—appears to be the main feature that catches the attention of walkers. Given that these verbatim transcripts from a small convenience sample reflect, with one exception, the emotions felt by women, the results cannot really be generalized. In the analyses, the anonymity of the avatars is preserved by using initials, even though in all the cases the participants consented to the reproduction of the emotions they expressed.

Recovering from chronic myeloid leukemia: the patients' perspective seen through the lens of narrative medicine.

The main objective of this study is to gain a deeper understanding of how patients suffering from chronic myeloid leukemia (CML) cope with their illness. The study aims to reconstruct the subjective meaning-making process related to CML in order to gain insights into the impact the disease has on patients' emotions and everyday lives, as well as to explore the psychological impact of their being presented with the chance to suspend their therapy and recover from the disease. Data were gathered from a qualitative study conducted in Italy on 158 Italian CML patients. Basing the study on the narrative inquiry approach, the patients were required to describe their patient journey in a qualitative narrative diary. These contained prompts to elicit the free expression of their needs, expectations, and priorities. A lexicographic analysis was carried out with T-LAB software and in particular a thematic analysis of elementary contexts (TAECs) and a word association analysis (WAA). The TAEC detected four thematic clusters related to two factors (temporal frame and contextual setting) that explained the variance among the narratives. The WAA evidenced a wide variety of emotions, both positive and negative, as patients reacted to the possibility of interrupting their therapy. A better understanding of patients' experiences can offer insights into promoting the development of more sustainable healthcare services and into therapeutic innovation aimed at improving patients' quality of life and at engaging them more in their treatment. The findings of this study can also help make medical professionals more aware of the patient's burden and help them identify potential interactions and emotional levers to improve clinical relationships.

Mindset, confidence and skill: The impact of a development intervention

This article reports changes in mindset and attitudes of a cohort of 798 NHS managers in relation to the implementation of a new NHS trust-wide performance appraisal system. This was measured through two methods: the statistical analysis of data collected before and after an intensive training/orientation intervention by means of χ2 tests; and a corpus linguistic analysis of word associations and text comments that is novel to the literature.

Die Repräsentation von Fachsprache im mentalen Lexikon. Empirische Ergebnisse zur Wortassoziation

The main purpose of the paper is to show how the psycholinguistic method of word association analysis can provide new insights into the mental representation of languages forspecific purposes. Languages for specific purposes and their use have been thoroughly analysed in the past decades. Despite the extensive research carried out in the field, just a few papers analyse the cognitive representation of these languages. The present paper shows that some characteristics of the cognitive representation of these languages can be obtained on the basis of word association tests. Tests have been carried out in Hungarian for two languages for specific purposes: Economy and Sports. In the investigation of the language of Economy we collected associations with a research webpage specially created for collecting word association data from internet users. The associations of 593 subjects are analysed for 19 stimuli. For the language of Sports 100 people gave associations to 100 stimuli, of which 20 are examined in the paper. The analyses of the associations show that the cognitive organization depends not only on education level in a specialised field: it seems that (at least up to a certain level) age also plays an important role in the cognitive organization structure of specialised languages. The final part of the paper shows how a better understanding of the cognitive representation of languages for specific purposes can help to improve selected aspects of specialised communication.

Do Intelligence, Intensity of Felt Emotions and Emotional Regulation Have an Impact on Life Satisfaction? A Quali-quantitative Study on Subjective Wellbeing with Italian Children Aged 8–11

According to the hedonic perspective of Positive Psychology and based on children’s literature, the aim of the present study is twofold: to investigate the factors which could elicit a sense of wellbeing in children; to assess the impact of personal factors (non-verbal intelligence, positive/negative emotions and regulation of positive/negative emotions) on children’s satisfaction in life. The participants were 132 Italian children between the ages of 8 and 11. Content analysis and quantitative analysis were conducted. Analysis of word associations revealed that the term wellbeing was associated with family, school, friendship, sports, the emotion of happiness, enjoyment, company, relaxation and feeling loved and protected. Analysis of specific typical associations revealed that females were more likely to describe wellbeing in socio-emotional terms (love/feeling safe) and boys in terms of play. Furthermore, the regression analyses showed that intensity of felt emotions had an impact on life satisfaction, while capacity to regulate emotions and non-verbal intelligence did not. It also pointed out the gender differences in subjective wellbeing: in boys the intensity of positive emotions triggered satisfaction with self; while in girls, the level of life satisfaction was explained both by high intensity of positive emotions and by low intensity of negative emotions. The results are discussed in order to highlight the importance of measuring and monitoring children’s life satisfaction to foster optimal conditions of life and to prevent maladaptive adjustment.

Qualitative questionnaire for the identification of cognems (Qualiquic): an exploratory technique to identify social representation contents and relations

While the investigation of social representation contents through the analysis of word associations is convenient, it does not directly inform about the nature of relationships that representation elements maintain with the social object. This paper presents a qualitative technique, Qualiquic, that is easy and simple to administer. Qualiquic has the advantage of gathering representation contents characterized by their relationships with the representation object, based on a simplified list of descriptive, practical, and evaluative connectors of the basic cognitive schemes model. The underlying theoretical principles are explained, and empirical guidelines are provided, as well as an empirical example of use.