scholarly journals Performance Analysis of Hyperparameters on a Sentiment Analysis Model

2020 ◽  
Vol 10 (4) ◽  
pp. 6016-6020
Author(s):  
I. A. Kandhro ◽  
S. Z. Jumani ◽  
F. Ali ◽  
Z. U. Shaikh ◽  
M. A. Arain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Analysis ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Course Evaluation ◽  
Experimental Results ◽  
Activation Functions ◽  
Analysis Model ◽  
Proposed Model ◽  
Evaluation Dataset

This paper focuses on the performance analysis of hyperparameters of the Sentiment Analysis (SA) model of a course evaluation dataset. The performance was analyzed regarding hyperparameters such as activation, optimization, and regularization. In this paper, the activation functions used were adam, adagrad, nadam, adamax, and hard_sigmoid, the optimization functions were softmax, softplus, sigmoid, and relu, and the dropout values were 0.1, 0.2, 0.3, and 0.4. The results indicate that parameters adam and softmax with dropout value 2.0 are effective when compared to other combinations of the SA model. The experimental results reveal that the proposed model outperforms the state-of-the-art deep learning classifiers.

scholarly journals A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 374
Author(s):  
Babacar Gaye ◽  
Dezheng Zhang ◽  
Aziguli Wulamu
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
State Of The Art ◽  
Accuracy Score ◽  
Learning Models ◽  
Proposed Model

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

A Deep Neural Network Model for Cross-Domain Sentiment Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 1-16
Author(s):  
Suman Kumari ◽  
Basant Agarwal ◽  
Mamta Mittal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Analysis Model ◽  
Cross Domain ◽  
Unstructured Text

Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning, and therefore, a labelled dataset is required to build the model, and it is very difficult task to obtain a labelled dataset for every domain. Cross-domain sentiment analysis is to develop a model which is trained on labelled dataset of one domain, and the performance is evaluated on another domain. The performance of such cross-domain sentiment analysis is still very limited due to presence of many domain-related terms, and the sentiment analysis is a domain-dependent problem in which words changes their polarity depending upon the domain. In addition, cross-domain sentiment analysis model suffers with the problem of large number of out-of-the-vocabulary (unseen words) words. In this paper, the authors propose a deep learning-based approach for cross-domain sentiment analysis. Experimental results show that the proposed approach improves the performance on the benchmark dataset.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

Wavelets as activation functions in Neural Networks

2021 ◽  
pp. 1-11
Author(s):  
Oscar Herrera ◽  
Belém Priego
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Performance ◽  
Research Area ◽  
Experimental Results ◽  
Activation Functions ◽  
Hyperbolic Tangent ◽  
Open Research ◽  
Bounded Functions

Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending the list of activation functions to wavelets which can be available in high performance platforms.

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 21-34
Author(s):  
Mostefai Abdelkader
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Learning Approach ◽  
Probabilistic Representation ◽  
Effective Manner ◽  
Textual Data ◽  
Positive Class ◽  
Negative Class ◽  
Deep Learning Model

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.

scholarly journals Multimodal Summarization with Guidance of Multimodal Reference

2020 ◽  
Vol 34 (05) ◽  
pp. 9749-9756
Author(s):  
Junnan Zhu ◽  
Yu Zhou ◽  
Jiajun Zhang ◽  
Haoran Li ◽  
Chengqing Zong ◽  
...  
Keyword(s):  
Objective Function ◽  
Evaluation Method ◽  
Reference Data ◽  
State Of The Art ◽  
Semantic Space ◽  
Experimental Results ◽  
Model Output ◽  
Proposed Model ◽  
Evaluation Metric

Multimodal summarization with multimodal output (MSMO) is to generate a multimodal summary for a multimodal news report, which has been proven to effectively improve users' satisfaction. The existing MSMO methods are trained by the target of text modality, leading to the modality-bias problem that ignores the quality of model-selected image during training. To alleviate this problem, we propose a multimodal objective function with the guidance of multimodal reference to use the loss from the summary generation and the image selection. Due to the lack of multimodal reference data, we present two strategies, i.e., ROUGE-ranking and Order-ranking, to construct the multimodal reference by extending the text reference. Meanwhile, to better evaluate multimodal outputs, we propose a novel evaluation metric based on joint multimodal representation, projecting the model output and multimodal reference into a joint semantic space during evaluation. Experimental results have shown that our proposed model achieves the new state-of-the-art on both automatic and manual evaluation metrics. Besides, our proposed evaluation method can effectively improve the correlation with human judgments.

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