News Popularity Prediction with Ensemble Methods of Classification

Khan, None Aasim; Worah, Gautam; Kothari, Mehul; Jadhav, Yogesh; Nimkar, Anant V.

doi:10.1109/icccnt.2018.8494095

Cited by 6 publications

(15 citation statements)

References 9 publications

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“…Videos and News [22] Videos [14] Videos [23] Images [21] Videos [9] Videos [39] News [13] News [10] News [15] Videos [11] News [16] Videos [40] C = Classification R = Regression.…”

Section: R Textual Visual Meta-data Content Type Referencesmentioning

confidence: 99%

“…Among them, Artificial Intelligence (AI) techniques that can find patterns relating the content and its variables to the popularity have obtained successful results lately. Mainly, methods of Machine Learning (ML), aided by Natural Language Processing (NLP) when one has textual content, are the subareas of AI mostly prominent to that task [10,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…In order to handle the textual content, ML methods require that they are transformed into a categorical or numerical representation. Concerning the popularity prediction task, NLP allows for designing linguistic-based features or discovering them directly from the content [10,16]. Recently, NLP techniques that transform written and spoken content into a vector representation embedded into a vector space have become the standard practice [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…The goal is to predict whether content will become popular or not; in most cases, only two classes are used: popular and non-popular. These predictive methods use Classification and are often called classifiers [13,15,16].…”

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confidence: 99%

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Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study

Sá

Rocha

Paes

2021

Sensors

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The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract the attention of most users. For example, in the data explored in this article, only 6% of the most popular videos account for 85% of total views. Finding out in advance which videos will be popular is not trivial, especially given many influencing variables. Nevertheless, a tool with this ability would be of great value to help dimension network infrastructure and properly recommend new content to users. In this way, this manuscript examines the machine learning-based approaches that have been proposed to solve the prediction of web content popularity. To this end, we first survey the literature and elaborate a taxonomy that classifies models according to predictive features and describes state-of-the-art features and techniques used to solve this task. While analyzing previous works, we saw an opportunity to use textual features for video prediction. Thus, additionally, we propose a case study that combines features acquired through attribute engineering and word embedding to predict the popularity of a video. The first approach is based on predictive attributes defined by resource engineering. The second takes advantage of word embeddings from video descriptions and titles. We experimented with the proposed techniques in a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of engineering features and embeddings using the Random Forest algorithm achieved the best result, with an accuracy of 87%.

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“…Videos and News [22] Videos [14] Videos [23] Images [21] Videos [9] Videos [39] News [13] News [10] News [15] Videos [11] News [16] Videos [40] C = Classification R = Regression.…”

Section: R Textual Visual Meta-data Content Type Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study

Sá

Rocha

Paes

2021

Sensors

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“…Support vector machine (SVM) and natural language processing (NLP) methods are utilized for prediction. The study [17] successfully implements and compares 11 models and finds that the gradient boost model performs best with 79.7% accuracy. They obtained the highest accuracy of 73% with random forest after testing five classification models, while the study achieved the accuracy of 78.1% using gradient boosting.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Prediction of YouTube Video Popularity Using XGBoost

et al. 2021

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YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos.

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