Journal ArticleDOI
Hybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter
TLDR
This study hybridize two n-gram models, unigram and n- gram, and applied Laplace smoothing to Naïve Bayesian classifier and Katz back-off on the model in order to smoothen and address the limitation of accuracy in terms of precision and recall of n- Gram models caused by the ‘zero count problem.’Abstract:
Twitter, an online micro-blogging and social networking service, provides registered users the ability to write in 140 characters anything they wish and hence providing them the opportunity to express their opinions and sentiments on events taking place. Politically sentimental tweets are top-trending tweets; whenever election is near, users tweet about their favorite candidates or political parties and at times give their reasons for that. In this study, we hybridize two n-gram [two n-gram models used in this study are unigram and n-gram. Therefore, in this study, where unigram is mentioned that refers to a least-order n-gram (unigram) and where n-gram is mentioned that refers to the highest-order (full sentence or tweet level) n-gram] models and applied Laplace smoothing to Naive Bayesian classifier and Katz back-off on the model. This was done in order to smoothen and address the limitation of accuracy in terms of precision and recall of n-gram models caused by the ‘zero count problem.’ Result from our baseline model shows an increase of 6.05% in average F-Harmonic accuracy in comparison with the n-gram model and 1.75% increase in comparison with the semantic-topic model proposed from a previous study on the same dataset, i.e., Obama–McCain dataset.read more
Citations
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Journal ArticleDOI
Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.
Waqas Haider Bangyal,Rukhma Qasim,Najeeb Ur Rehman,Zeeshan Ahmad,Hafsa Dar,Laiqa Rukhsar,Zahra Aman,Jamil Ahmad +7 more
TL;DR: In this article, a semantic model with term frequency and inverse document frequency weighting for data representation was used to predict the sentiment class of each fake news on COVID-19.
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Movie Revenue Prediction Based on Purchase Intention Mining Using YouTube Trailer Reviews
TL;DR: This paper builds a model for movie revenue prediction prior to the movie's release using YouTube trailer reviews and proves the superiority of this approach compared to three baseline approaches and achieved a relative absolute error of 29.65%.
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A Hybrid Metaheuristic Method in Training Artificial Neural Network for Bankruptcy Prediction
TL;DR: Two metaheuristics algorithms, Magnetic Optimization Algorithm and Particle Swarm Optimization (PSO) have been enhanced through hybridization to propose a new method MOA-PSO, which exhibits promising results with a faster and more accurate prediction, with 99.7% accuracy.
Journal ArticleDOI
Co-attention networks based on aspect and context for aspect-level sentiment analysis
TL;DR: Zhang et al. as discussed by the authors proposed a co-attention mechanism to capture the interactions between aspect and context, which interactively concentrates the semantic influences on context and aspect to generate greater informative representation.
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A Survey on Machine Learning Techniques in Movie Revenue Prediction
Ibrahim Said Ahmad,Ibrahim Said Ahmad,Azuraliza Abu Bakar,Mohd Ridzwan Yaakub,Shamsuddeen Hassan Muhammad,Shamsuddeen Hassan Muhammad +5 more
TL;DR: The review analysis found out that US cinema attracted the highest number of publications, followed by the Chinese cinema, Korean cinema, and Indian cinema in that order, and regression, classification and clustering data mining approaches were used in the reviewed articles.
References
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