Google searches and twitter mood: nowcasting telecom sales performance

Bughin, Jacques

doi:10.1007/s11066-015-9096-5

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…With the boom in information technology, online social networks like Twitter, Facebook and Weibo have become closely related to people's daily life [17], [39]. Digital marketing, especially caused by online social networks, has become one of the most important way for businesses.…”

Section: The Impact Of Human Activitiesmentioning

confidence: 99%

A Data-Driven Gross Domestic Product Forecasting Model Based on Multi-Indicator Assessment

Zhang

Chang

et al. 2021

IEEE Access

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Gross domestic product (GDP) is a general reference to comprehensive measure the level of a country or region's economic development and diagnoses the health of economy. Traditional economic census-based methods for GDP forecasting are often expensive and resource-consuming, more importantly, economic census results lag significantly. This paper presents a data-driven GDP forecasting model that integrates multidimensional data from the aspects of electricity consumption, climate and human activities. Specifically, the model is built based the long-short-term-memory neural network with particle swarm optimization algorithm. The input multidimensional data are analyzed by correlation-based feature selection, and then filtered to five influencing factors. The experimental results show that these influencing factors are obviously related to economic development, at the same time, GDP can be well predicted based on the proposed model in a timely and relatively accurate manner.

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Section: The Impact Of Human Activitiesmentioning

confidence: 99%

A Data-Driven Gross Domestic Product Forecasting Model Based on Multi-Indicator Assessment

Zhang

Chang

et al. 2021

IEEE Access

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“…This type of data has been used to predict movie box-office revenues [41]. Bughin [42] finds that the valence of eWOM influences sales more than Google Trends. Geva et al [5] find that adding Google search data to models based on the more commonly used eWOM data improves the accuracy of sale predictions for search products.…”

Section: Ewom-based and Gsi-based Sales Predictionmentioning

confidence: 99%

Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

et al. 2019

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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.

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“…However, search engines are not the only online source for Internet-based economic indicators. For instance, social network activities have been used as a predictor of the evolution of the stock market [8,1], box office revenues [37,34] or telecom sales [9].…”

Section: Related Workmentioning

confidence: 99%

Monitoring e-commerce adoption from online data

et al. 2018

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The purpose of this paper is to propose an intelligent system to automatically monitor the firms' engagement in e-commerce by analyzing online data retrieved from their corporate websites. The design of the proposed system combines web content mining and scraping techniques with learning methods for Big Data. Corporate websites are scraped to extract more than 150 features related to the e-commerce adoption, such as the presence of some keywords or a private area. Then, these features are taken as input by a classification model that includes dimensionality reduction techniques. The system is evaluated with a data set consisting of 426 corporate websites of firms based in France and Spain. The system successfully classified most of the firms into those that adopted e-commerce and those that did not, reaching a classification accuracy of 90.6%. This demonstrates the feasibility of monitoring e-commerce adoption from online data. Moreover, the proposed system represents a cost-effective alternative to surveys as method for collecting e-commerce information from companies, and is capable of providing

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Google searches and twitter mood: nowcasting telecom sales performance

Cited by 13 publications

References 37 publications

A Data-Driven Gross Domestic Product Forecasting Model Based on Multi-Indicator Assessment

A Data-Driven Gross Domestic Product Forecasting Model Based on Multi-Indicator Assessment

Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

Monitoring e-commerce adoption from online data

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