Which Configuration Works Best? An Experimental Study on Supervised Arabic Twitter Sentiment Analysis

2015 ◽  
Author(s):  
Talaat Khalil ◽  
Amal Halaby ◽  
Muhammad Hammad ◽  
Samhaa R. El-Beltagy
Keyword(s):  
Experimental Study ◽  
Sentiment Analysis

scholarly journals Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

2019 ◽  
Vol 44 (2) ◽  
pp. 151-178 ◽  
Author(s):  
Mateusz Lango
Keyword(s):  
Experimental Study ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Sentiment Classification ◽  
Important Task ◽  
Imbalanced Learning ◽  
Learning Methods ◽  
Feature Representations ◽  
The Impact

Abstract Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors — commonly studied in imbalanced learning — are investigated on sentiment corpora to evaluate the impact of class imbalance.

scholarly journals Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3883
Author(s):  
Spyridon Kardakis ◽  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Training Methods ◽  
Learning Models ◽  
Neural Models

Attention-based methods for deep neural networks constitute a technique that has attracted increased interest in recent years. Attention mechanisms can focus on important parts of a sequence and, as a result, enhance the performance of neural networks in a variety of tasks, including sentiment analysis, emotion recognition, machine translation and speech recognition. In this work, we study attention-based models built on recurrent neural networks (RNNs) and examine their performance in various contexts of sentiment analysis. Self-attention, global-attention and hierarchical-attention methods are examined under various deep neural models, training methods and hyperparameters. Even though attention mechanisms are a powerful recent concept in the field of deep learning, their exact effectiveness in sentiment analysis is yet to be thoroughly assessed. A comparative analysis is performed in a text sentiment classification task where baseline models are compared with and without the use of attention for every experiment. The experimental study additionally examines the proposed models’ ability in recognizing opinions and emotions in movie reviews. The results indicate that attention-based models lead to great improvements in the performance of deep neural models showcasing up to a 3.5% improvement in their accuracy.

Experimental study on the restoration from a tilt-azimuth series for improvement in resolution

1995 ◽  
Vol 53 ◽  
pp. 636-637
Author(s):  
Norio Baba ◽  
Norihiko Ichise ◽  
Syunya Watanabe
Keyword(s):  
Experimental Study ◽  
Spherical Aberration ◽  
Incident Beam ◽  
Optical Parameters ◽  
Beam Tilting ◽  
Illumination Method ◽  
Restoration Method ◽  
Illumination Mode

The tilted beam illumination method is used to improve the resolution comparing with the axial illumination mode. Using this advantage, a restoration method of several tilted beam images covering the full azimuthal range was proposed by Saxton, and experimentally examined. To make this technique more reliable it seems that some practical problems still remain. In this report the restoration was attempted and the problems were considered. In our study, four problems were pointed out for the experiment of the restoration. (1) Accurate beam tilt adjustment to fit the incident beam to the coma-free axis for the symmetrical beam tilting over the full azimuthal range. (2) Accurate measurements of the optical parameters which are necessary to design the restoration filter. Even if the spherical aberration coefficient Cs is known with accuracy and the axial astigmatism is sufficiently compensated, at least the defocus value must be measured. (3) Accurate alignment of the tilt-azimuth series images.

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