Big social data as a service (BSDaaS): a service composition framework for social media analysis

Ali, Kashif; Hamilton, Margaret; Thevathayan, Charles; Zhang, Xiuzhen

doi:10.1186/s40537-022-00620-4

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…Therefore, they propose a framework that dynamically processes and analyses social media data according to its characteristics. This framework includes collecting data from social media, cleaning noise data, sentiment analysis, opinion analysis, and text analysis and presenting it to the user (Ali et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

SUR,

ÇAKIR

2023

OPUS Toplum Araştırmaları Dergisi

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This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods' time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the model’s purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords "flood", "meter", "rain", "irrigation", "infrastructure", "sewerage", "sewage", "maintenance hole ", "aski", "waterless", "water" were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

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Section: Related Workmentioning

confidence: 99%

Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

SUR,

ÇAKIR

2023

OPUS Toplum Araştırmaları Dergisi

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“…The research topic of many papers also covers the presidential elections in the USA [9,10] and other countries [11,12]. At the same time, the works studying and describing complex social analytics platforms, such as [13], are not fully presented.…”

Section: Introductionmentioning

confidence: 99%

On the development of an information system for monitoring user opinion and its role for the public

et al. 2022

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem’s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s moods and attitudes to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.

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Big social data as a service (BSDaaS): a service composition framework for social media analysis

Cited by 2 publications

References 43 publications

Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

On the development of an information system for monitoring user opinion and its role for the public

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