A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng; Jiang, Fengling; Kang, Bin; Hussain, Amir

doi:10.1016/j.neucom.2019.10.009

Cited by 110 publications

(61 citation statements)

References 39 publications

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“…The issue of automatic moderation is complicated by the complexity of the task and creative use of language. The state-of-the-art technology for the related task of sentiment detection shows a combined measure of precision and recall between 0.60 and 0.89 (and similar ranges for accuracy) for a wide range of algorithms used on controlled datasets on product and hotel reviews [67]. Chen et al [68] used Convolutional Neural Networks, with some preprocessing, to detect verbal aggression in Twitter comments with similar results on their test sets.…”

Section: Discussionmentioning

confidence: 92%

Anonymity and Inhibition in Newspaper Comments

Knustad

Johansson

2021

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Newspaper comment sections allow readers to voice their opinion on a wide range of topics, provide feedback for journalists and editors and may enable public debate. Comment sections have been criticized as a medium for toxic comments. Such behavior in comment sections has been attributed to the effect of anonymity. Several studies have found a relationship between anonymity and toxic comments, based on laboratory conditions or the comparison of comments from different sites or platforms. The current study uses real-world data sampled from The Washington Post and The New York Times, where anonymous and non-anonymous users comment on the same articles. This sampling strategy decreases the possibility of interfering variables, ensuring that any observed differences between the two groups can be explained by anonymity. A small but significant relationship between anonymity and toxic comments was found, though the effects of both the newspaper and the direction of the comment were stronger. While it is true that non-anonymous commenters write fewer toxic comments, we observed that many of the toxic comments were directed at others than the article or author of the original article. This may indicate a way to restrict toxic comments, while allowing anonymity, by restricting the reference to others, e.g., by enforcing writers to focus on the topic.

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Section: Discussionmentioning

confidence: 92%

Anonymity and Inhibition in Newspaper Comments

Knustad

Johansson

2021

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“…The deep learning can be used in different various application such as cyber-security, sentiment analysis, speech enhancement and etc. [31,32,33,34,35,36,37,38] . However, in this paper we proposed a novel framework to detect posture prediction.…”

Section: Deep Learning (Dl) Methodsmentioning

confidence: 99%

A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

et al. 2021

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The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%.

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“…Additionally, in more general task assignment, the authors of [31] proposed a new parallel allocation of delay-tolerant tasks in practical crowdsourcing systems. As an example of a crowdsourced dataset, the authors of [32] conducted sentiment analysis experiments on a product reviews dataset [33] in the Persian language.…”

Section: Related Workmentioning

confidence: 99%

Iterative Learning for K-Approval Votes in Crowdsourcing Systems

2021

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Crowdsourcing systems have emerged as cornerstones to collect large amounts of qualified data in various human-powered problems with a relatively low budget. In eliciting the wisdom of crowds, many web-based crowdsourcing platforms have encouraged workers to select top-K alternatives rather than just one choice, which is called “K-approval voting”. This kind of setting has the advantage of inducing workers to make fewer mistakes when they respond to target tasks. However, there is not much work on inferring the correct answer from crowd-sourced data via a K-approval voting. In this paper, we propose a novel and efficient iterative algorithm to infer correct answers for a K-approval voting, which can be directly applied to real-world crowdsourcing systems. We analyze the average performance of our algorithm, and prove the theoretical error bound that decays exponentially in terms of the quality of workers and the number of queries. Through extensive experiments including the mixed case with various types of tasks, we show that our algorithm outperforms Expectation and Maximization (EM) and existing baseline algorithms.

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A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

Cited by 110 publications

References 39 publications

Anonymity and Inhibition in Newspaper Comments

Anonymity and Inhibition in Newspaper Comments

A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

Iterative Learning for K-Approval Votes in Crowdsourcing Systems

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