Utilization of sentiment analysis to assess and compare negative finding reporting in veterinary and human literature

Myszewski, Joshua J.; Schroeder, Kristopher M.; Schroeder, Carrie A.

doi:10.1016/j.rvsc.2022.04.010

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…more positively skewed in veterinary clinical trials than in human trials [14] whilst text mining, including keyword extraction, has demonstrated good results when combining or analysing veterinary patient records [15]. Text mining techniques have also been used to construct an electronic resource containing veterinary terminology that will allow automated monitoring of free-text veterinary reports [16], assist with monitoring trends and risk factors in equine antimicrobial prescriptions [17], and identify the main research topics within Mountain livestock farming [18].…”

Section: Plos Onementioning

confidence: 99%

“…Whilst text mining has been used to conduct a review of peer reviewed suicide research [ 13 ], it has not been applied so thoroughly in relation to veterinary mental health. Sentiment analysis has been used to show that the publication bias toward reporting significant results is more positively skewed in veterinary clinical trials than in human trials [ 14 ] whilst text mining, including keyword extraction, has demonstrated good results when combining or analysing veterinary patient records [ 15 ]. Text mining techniques have also been used to construct an electronic resource containing veterinary terminology that will allow automated monitoring of free-text veterinary reports [ 16 ], assist with monitoring trends and risk factors in equine antimicrobial prescriptions [ 17 ], and identify the main research topics within Mountain livestock farming [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice

Duncan,

Henry,

Lamont

2024

PLoS ONE

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Background The aim of the How Farm Vets Cope project was to co-design, with farm veterinary surgeons, a set of web-based resources to help them and others deal with the different situations that they can face. As part of the wider project, participants were recruited for one-to-one semi-structured phone interviews. These interviews focused on elements of job satisfaction and how the participants coped during periods of poor mental wellbeing or with setbacks and failure. Methods Transcripts of these interviews were analysed using both quantitative methods of sentiment analysis and text mining, including term frequency/inverse document frequency and rapid automated keyword extraction, and qualitative content analysis. The twin aims of the analysis were identifying the important themes discussed by the participants and comparing the results of the two methods to see what differences, if any, arose. Results Analysis using the afinn and nrc sentiment lexicons identified emotional themes of anticipation and trust. Rapid automated keyword extraction highlighted issues around age of vets and support, whilst using term frequency/inverse document frequency allowed for individual themes, such as religion, not present across all responses, to be identified. Content analysis supported these findings, pinpointing examples of trust around relationships with farmers and more experienced vets, along with some examples of the difference good support networks can make, particularly to younger vets. Findings This work has confirmed previous results in identifying the themes of trust, communication and support to be integral to the experience of practicing farm veterinary surgeons. Younger or less experienced vets recognised themselves as benefiting from further support and signposting, leading to a discussion around the preparation of veterinary students for entry into a farm animal vet practice. The two different approaches taken showed very good agreement in their results. The quantitative approaches can be scaled to allow a larger number of interviews to be utilised in studies whilst still allowing the important qualitative results to be identified

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice

Duncan,

Henry,

Lamont

2024

PLoS ONE

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“…They devised a method for learning iterative updates to encode contextual information into task representation, as well as a metalearner. During the updating phase, the model is improved, and the final representation is utilized to limit the decoder to make predictions on unlabeled data, resulting in the best performance on the benchmark dataset tiered ImageNet [ 17 ]. On the basis of MAML, the authors [ 15 ] established the idea of “task robustness.” They reasoned that metalearning approaches were only effective in a few particular distribution tasks, thus they recreated MAML using a new formula, resulting in a stable model.…”

Section: Related Workmentioning

confidence: 99%

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Bhende¹,

Thakare²,

Pant

et al. 2022

Computational Intelligence and Neuroscience

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With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.

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“…Utilization of sentiment analysis (Myszewski et al, 2022) The purpose of this study was to utilize a novel SA tool as a quantitative measure for assessing clinical trial reporting trends in human and veterinary medical literature, using the GAN-BioBERT sentiment classi er.…”

Section: Recent Papers Exploring Sentiment Analysis In Social Mediamentioning

confidence: 99%

A bibliometric analysis to explore sentiment analysis in the domain of social media research

Dahish¹,

Miah²

2022

Preprint

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Businesses use sentiment analysis in different ways for generating market or consumer insights to improve their performance. Existing sentiment analysis studies related to social media research have been significant due to the fact of their rapidly growing potential, as this type of analysis of social media content represents immense benefits. It is imperative to explore the need for such analysis to delineate the trend holistically for knowledge landscaping purposes. The study addresses the research gap through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the performance, content, and thematic analysis. We adopt an automated bibliometric study approach using the R-tool- biblioshiny for generating and presenting outcomes. Finding points on the vital usages of sentiment analysis such as innovation, transparency, and improved efficiency. It also highlights the uniqueness of sentiment analysis for synthesizing social media content to examine various aspects such as the knowledge-domain map that detects author collaboration networks as well as journal relationships.

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Utilization of sentiment analysis to assess and compare negative finding reporting in veterinary and human literature

Cited by 4 publications

References 19 publications

Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice

Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

A bibliometric analysis to explore sentiment analysis in the domain of social media research

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