Studying social media sentiment using human validated analysis

Lappeman, James; Clark, Robyn; Evans, James B.; Sierra-Rubia, Lara; Gordon, Patrick

doi:10.1016/j.mex.2020.100867

Cited by 26 publications

(16 citation statements)

References 15 publications

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“…Content analysis was applied using both qualitative and quantitative methods (Bryman, 2006). Through combining qualitative natural language processing (NLP) with human validation, the data's precision increases (McKenzie and Swails, 2016; Lappeman et al , 2020). In particular, the larger numerical data, like conversation volume and net sentiment calculations, were analysed quantitatively.…”

Section: Methodsmentioning

confidence: 99%

“…Nonetheless, understanding consumer perceptions of service and its impact on reputation are an important driver of success in service industries (Meesala and Paul, 2018). Sentiment analysis has become a growing means of measuring service and reputation (Sari et al , 2018; Lappeman et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sentiment analysis was applied, using machine learning programmes (such as NLP algorithms; McKenzie and Swails, 2016) and a lexicon to gauge social media sentiment of the five largest retail banks in South Africa (Dhaoui et al , 2017). The methodological process was enhanced by using human validation techniques in order to increase data precision and to ensure that data were correctly categorised (McKenzie and Swails, 2016; Lappeman et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

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The effect of nWOM firestorms on South African retail banking

Lappeman

Clark

Evans

et al. 2021

IJBM

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PurposeThis study analysed the effect of online negative word-of-mouth (nWOM) firestorms in the retail banking sector. By understanding negative sentiment and sentiment recovery across an entire retail banking sector, the research exposed a unique view of banking in South Africa.Design/methodology/approachThe study made use of both a sentiment and topic analysis of over 1.7 million social media posts in South Africa. The methodology made use of both NLP and human validation techniques to measure changes in social media sentiment during online firestorms. This measurement included each of South Africa's major retail banks over a twelve month period.FindingsFrom the analysis, key trigger characteristics for these firestorms (product failures, service failures, social failures and communication failures) were categorised. In addition, the average duration of a firestorm was calculated and factors that impact sentiment recovery were explored.Originality/valueThe study was located in South Africa and, unlike firm level studies, researched nWOM for the whole retail banking sector. A theoretical footprint depicting the typical anatomy of a firestorm was derived in order to aid stakeholders to be more vigilant and better equipped to provide correct intervention in such times of crisis.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The effect of nWOM firestorms on South African retail banking

Lappeman

Clark

Evans

et al. 2021

IJBM

Self Cite

View full text Add to dashboard Cite

show abstract

“…This type of NLP is like that available on platforms like Amazon Lex, IBM Watson Assistant and DialogFlow. With the use of BrandsEye's custom interface, a sub-sample of posts were sent for human topic analysis and sentiment validation 25,26 . This methodology of sub-sample validation improves the accuracy of net-sentiment measurement and topic analysis as NLP is still not accurate enough for precise interpretation of slang, sarcasm and emoji's 25 .…”

Section: Methodsmentioning

confidence: 99%

“…With the use of BrandsEye's custom interface, a sub-sample of posts were sent for human topic analysis and sentiment validation 25,26 . This methodology of sub-sample validation improves the accuracy of net-sentiment measurement and topic analysis as NLP is still not accurate enough for precise interpretation of slang, sarcasm and emoji's 25 . Mentions were analysed by the human raters (a large, distributed workforce who BrandsEye curate and pay to verify and mark-up raw social media data) and a set of themes were generated.…”

Section: Methodsmentioning

confidence: 99%

Negative sentiment towards COVID-19 vaccines: A comparative study of USA and UK social media posts before vaccination rollout

2021

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Introduction: The global spread of the COVID-19 pandemic was rapid and devastating to humanity. The public health response to the pandemic was rapid too. Completion of COVID-19 vaccine development was achieved in under a year. The USA and the UK were the first countries to rollout COVID-19 vaccines to contain the pandemic. Successful rollout of the vaccines hinges on many factors, among which is public trust. Aim: To investigate the sentiments towards COVID-19 vaccines in the USA and UK prior to vaccination rollout. Methods: Neuro-linguistic programming with human validation was used to analyse a sample of 243,883 COVID-19 vaccine related social media posts from the USA and the UK in the period 28 July to 28 August 2020. The sentiment analysis measured polarity (positive, neutral, negative), and the themes present in negative comments. Results: In the sample of 243,883 social media posts, both the USA and the UK had a net sentiment profile of approximately 28% positive, 8% negative and 63% neutral sentiment. On further analysis, there were distinct differences between the two country’s social media sentiment towards COVID-19 vaccines. The differences were seen in the themes behind the negative sentiment. In the USA, the negative sentiments were mainly due to health and safety concerns, the fear of making a vaccine mandatory, and the role that pharmaceutical companies would play with the release of vaccines. In the UK the main driver of negative sentiment was the fear of making the vaccine mandatory (almost double the size of the sentiment in the USA). Conclusions: Negative sentiments towards COVID-19 vaccines were prevalent in the third quarter of 2020 in the USA and the UK. Reasons behind the negative sentiments can be used by authorities in the two countries to design evidence-based interventions to address the refusal of vaccination against COVID-19.

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Online media sentiment analysis for US oysters

Bradford,

Quagrainie

2024

Aquaculture Fish & Fisheries

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Sentiment analysis, a form of data analytics, utilises information from online discussions, reviews, and social media posts to assess consumer sentiments. This study utilised data collected from social media using online listening procedures to assess online sentiments on oysters from January 2019 through December 2022. The analysis utilises machine learning algorithms to extract consumer sentiments, opinions, and demands from online chatter from different online domains. The online sentiments are determined as positive, negative, or neutral based on their word choice, tone, and context. The information provided gives insights into perception, which is valuable information for oyster producers, seafood industry stakeholders, and marketers to identify consumer preferences and formulate appropriate strategies accordingly. The results suggest that while farmed oysters are gaining popularity, there are still some concerns and criticisms around the industry. Positive words associated with mentions of oysters in general include ‘great’, ‘love’, ‘delicious’, ‘enjoyed’, and ‘oyster bar’, while negative words associated with oysters include ‘water’, ‘raw oyster’, ‘bad’, and ‘not eat’. The overall percentage net sentiment associated with all oysters in the United States is positive at 63%. The net sentiment associated with wild oysters is positive, at 51%, and that of farmed oysters is 58%. The oyster industry could invest more in public education, sustainability, and water‐cleaning initiatives to improve its image. Utilising social media to monitor and shape public perception can help the industry address concerns and enhance oyster‐related sentiments, offering valuable insights for marketing and sales strategies.

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Studying social media sentiment using human validated analysis

Cited by 26 publications

References 15 publications

The effect of nWOM firestorms on South African retail banking

The effect of nWOM firestorms on South African retail banking

Negative sentiment towards COVID-19 vaccines: A comparative study of USA and UK social media posts before vaccination rollout

Online media sentiment analysis for US oysters

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