A multi-label ensemble predicting model to service recommendation from social media contents

Jain, Praphula Kumar; Pamula, Rajendra; Yekun, Ephrem Admasu

doi:10.1007/s11227-021-04087-7

Cited by 28 publications

(14 citation statements)

References 35 publications

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“…DT [ 66 ] is a supervised learning method that uses a set of rules to make decisions the same way a person makes decisions. This method divides a data set by features and answers specific questions until all data points belong to a particular class.…”

Section: Machine Learning Methodsmentioning

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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Section: Machine Learning Methodsmentioning

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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“…Guo et al [ 20 ] proposed a random-forest-based RS that used subscriber information, such as age, education, relationship, and occupation, from an insurance company dataset and verified it with an error rate of 0.16. Jain et al [ 21 ] proposed a voting-based RS using airline reviews on the SKYTRAX dataset and verified it with accuracy of 82.7%. Shahbazi et al [ 22 ] proposed an XGBoost-based RS using user-clicked information from an online shopping mall dataset, and verified it with accuracy of 89.6%.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Context-Aware Recommendation System for Tourism

Yoon

Choi

2023

Sensors

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Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.

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“…The results showed that the index system and early warning model were feasible and could provide a reference for the related NPO research. Jain et al [ 12 ] investigated the service recommendation model based on the multi-tag integration strategy and designed a predictive recommendation method. The designed method could predict consumer recommendations in travel and tourism.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

Chen

Zhang

2022

J Supercomput

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A multi-label ensemble predicting model to service recommendation from social media contents

Cited by 28 publications

References 35 publications

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

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

Real-Time Context-Aware Recommendation System for Tourism

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

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