Comparative analysis on Facebook post interaction using DNN, ELM and LSTM
12
TL;DR: A novel research approach to predict user interaction for social media post using machine learning algorithms using word2vec and doc2vec model to analyse the best approach for generating word embeddings.
read more
Abstract: This study presents a novel research approach to predict user interaction for social media post using machine learning algorithms. The posts are converted to vector form using word2vec and doc2vec model. These two methods are used to analyse the best approach for generating word embeddings. The generated word embeddings of post combined with other attributes like post published time, type of post and total interactions are used to train machine learning algorithms. Deep neural network (DNN), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) are used to compare the prediction of total interaction for a particular post. For word2vec, the word vectors are created using both continuous bag-of-words (CBOW) and skip-gram models. Also the pre-trained word vectors provided by google is used for the analysis. For doc2vec, the word embeddings are created using both the Distributed Memory model of Paragraph Vectors (PV-DM) and Distributed Bag of Words model of Paragraph Vectors (PV-DBOW). A word embedding is also created using PV-DBOW combined with skip-gram.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Characterizing Toxicity on Facebook Comments in Brazil
Samuel S. Guimarães,Julio Cesar dos Reis,Filipe N. Ribeiro,Fabrício Benevenuto +3 more
- 30 Nov 2020
TL;DR: This work provides a large-scale diagnostic about the toxicity in comments associated with news shared on Facebook during a major political event in Brazil, the release of Former President Lula from prison, using the Perspective API from Google to measure the toxicity of the comments and posts.
21
Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China
TL;DR: Wang et al. as mentioned in this paper explored appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model and multivariate Long Short-Term Memory Neural Network (LSTM) models.
A Complex Neural Network Model for Predicting a Personal Success based on their Activity in Social Networks
Fail Gafarov,Konstantin Nikolaev,Pavel N. Ustin,Andrey Anatolyevich Berdnikov,Valeria L. Zakharova,Sergey A. Reznichenko +5 more
TL;DR: In this work, the set of input parameters is gradually expanded to include metrics of users’ personal pages, and the results serve as material for an information and analytical system for automatic forecasting of human life activity based on the metrics of his personal profile in the social network VKontakte.
Classification of Abusive Thai Language Content in Social Media Using Deep Learning
Ruangsung Wanasukapunt,Suphakant Phimoltares +1 more
- 30 Jun 2021
TL;DR: This article presented binomial and multinomial models for Thai language abusive speech classification in social media and achieved F1 scores of 0.8510 and 0.9067, respectively.
6
Characterizing political bias and comments associated with news on Brazilian Facebook
TL;DR: In this paper, a detailed diagnostic of news stories and political opinions shared on Facebook, focusing on Brazilian pages, is presented, and an in-depth characterization of the political bias, audience demographics, reactions in posts, and toxicity of the comments of a sample of mainstream media, alternative media, and public figures.
2
References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
99K
Glove: Global Vectors for Word Representation
Jeffrey Pennington,Richard Socher,Christopher D. Manning +2 more
- 01 Oct 2014
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
•Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov,Ilya Sutskever,Kai Chen,Greg S. Corrado,Jeffrey Dean +4 more
- 05 Dec 2013
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
•Posted Content
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.