Business competitive analysis using promoted post detection on social media

Arora, Anuja; Srivastava, Aman; Bansal, Shivam

doi:10.1016/j.jretconser.2019.101941

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…The training set is randomly generated. RF is implemented to reduce the correlation between the random distributions of the input set and improve the bagging [91], [92]. Some techniques can improve the RF algorithm; for instance, nonparametric RF is more robust to outliers compared to other bagging or boosting algorithms [92].…”

Section: ) Ensemble Methodsmentioning

confidence: 99%

“…XGB is an efficient model for making decisions from a large dataset, and it is obtained by recursively partitioning the data space and fitting the prediction model at each partition. The individual decisions are entirely inaccurate but are better than those generated randomly [91]. RF combines several decision trees at the end of the process, whereas gradient boosting combines decision trees at the beginning of the process [93].…”

Section: ) Ensemble Methodsmentioning

confidence: 99%

“…Forecasting models are also used to draw predictions regarding the success of bank telemarketing sales of time deposits [83], the volume of call center arrivals' calls [56], [92] or sales volume [59]. Studies also carried out comparisons and predictions regarding the influence of social networks on social media promoted posts detection [73], [91], determinants of trust in s-commerce based on social presence and social support [156], the discrepancy between the evaluation of online and live products [77]. Other studies focused on predicting satisfaction and brand recommendation as well as purchase intention [157] and how natural-look campaigns are associated with the increase in artificial beauty practices [158].…”

Section: Forecastingmentioning

confidence: 99%

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Machine Learning and Marketing: A Systematic Literature Review

2022

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Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008-2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.

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Section: ) Ensemble Methodsmentioning

confidence: 99%

Section: ) Ensemble Methodsmentioning

confidence: 99%

Section: Forecastingmentioning

confidence: 99%

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Machine Learning and Marketing: A Systematic Literature Review

2022

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“…Stacking, on the other hand, is relatively complex. It first uses the basic learner to fit the data in the training set to generate the underlying model and then uses the prediction value generated by the basic learner in the first layer as the input of the second layer [26]. Unlike voting fusion, it can be regarded as heterogeneous integration, and it is an advantage set at the algorithm level.…”

Section: Voting Fusion Includes Mean Fusion Which Uses Multiple Group...mentioning

confidence: 99%

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Lv¹,

Han²,

Hu³

2023

JTST

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Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

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“…Several teams participated in the challenge and out of all One research work presented in this challenge was by Wang and Zhang (2017) on collected Flicker Dataset. In this research work, gradient boosting regression tree (GBRT) used ridge regression for prediction score generation by modelling the linear regression (Gerasimovic & Bugaric, 2018; Arora et al, 2020) in between features. Overall 10 features—user id (distinct key), post‐date, number of comments, hashtags, length of title, length of description, tag count, average view, group count and average member count are used to predict population using ridge regression method (Wang & Zhang, 2017).…”

Section: Related Research and Supporting Evidencementioning

confidence: 99%

A novel multimodal online news popularity prediction model based on ensemble learning

et al. 2023

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The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this article, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this article, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection and effective modelling to create a robust news popularity Prediction Model. Three kinds of features—meta‐features, text features and image features are used to design an influential and robust model. The relative error performance measure Root Mean Squared logarithmic error (RMSLE) is used to quantify the popularity prediction error. Further, the RMSLE outcome shows 0.351 which is the lowest error value given by the proposed model. Further, the most important features are also sought out to show the dependence of the best‐fit model on text and image features.

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Business competitive analysis using promoted post detection on social media

Cited by 17 publications

References 38 publications

Machine Learning and Marketing: A Systematic Literature Review

Machine Learning and Marketing: A Systematic Literature Review

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

A novel multimodal online news popularity prediction model based on ensemble learning

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