scholarly journals Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1880
Author(s):  
Samuel Terra Vieira ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Miguel Arjona Ramírez ◽  
Muhammad Saadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Monitoring System ◽  
Online Social Networks ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Base Station ◽  
Quality Monitoring ◽  
Telecommunication Services

A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals A multi domains short message sentiment classification using hybrid neural network architecture

2021 ◽  
Vol 10 (4) ◽  
pp. 2181-2191
Author(s):  
Devi Munandar ◽  
Andri Fachrur Rozie ◽  
Andria Arisal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Short Term Memory ◽  
Limited Resource ◽  
Short Message ◽  
Short Text ◽  
Domain Specific ◽  
Bahasa Indonesia

Sentiment analysis of short texts is challenging because of its limited context of information. It becomes more challenging to be done on limited resource language like Bahasa Indonesia. However, with various deep learning techniques, it can give pretty good accuracy. This paper explores several deep learning methods, such as multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and builds combinations of those three architectures. The combinations of those three architectures are intended to get the best of those architecture models. The MLP accommodates the use of the previous model to obtain classification output. The CNN layer extracts the word feature vector from text sequences. Subsequently, the LSTM repetitively selects or discards feature sequences based on their context. Those advantages are useful for different domain datasets. The experiments on sentiment analysis of short text in Bahasa Indonesia show that hybrid models can obtain better performance, and the same architecture can be directly used in another domain-specific dataset.

scholarly journals Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 113-121
Author(s):  
Firman Pradana Rachman
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

scholarly journals Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM)

2021 ◽  
Author(s):  
Usha Devi G ◽  
Priyan M K ◽  
Gokulnath Chandra Babu ◽  
Gayathri Karthick
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Twitter Data ◽  
Learning Techniques ◽  
Stop Word ◽  
Long Short Term Memory

Abstract Twitter sentiment analysis is an automated process of analyzing the text data which determining the opinion or feeling of public tweets from the various fields. For example, in marketing field, political field huge number of tweets is posting with hash tags every moment via internet from one user to another user. This sentiment analysis is a challenging task for the researchers mainly to correct interpretation of context in which certain tweet words are difficult to evaluate what truly is negative and positive statement from the huge corpus of tweet data. This problem violates the integrity of the system and the user reliability can be significantly reduced. In this paper, we identify the each tweet word and we are assigning a meaning into it. The feature work is combined with tweet words, word2vec, stop words and integrated into the deep learning techniques of Convolution neural network model and Long short Term Memory, these algorithms can identify the pattern of stop word counts with its own strategy. Those two models are well trained and applied for IMDB dataset which contains 50,000 movie reviews. With huge amount of twitter data is processed for predicting the sentimental tweets for classification. With the proposed methodology, the samples are experimentally collected from the real-time environment can be discriminated well and the efficacy of the system is improved. The result of Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%.

scholarly journals Implementation of Deep Learning Based Sentiment Classification and Product Aspect Analysis

2021 ◽  
Vol 40 ◽  
pp. 03032
Author(s):  
Shweta Dhabekar ◽  
M. D. Patil
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Short Form ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Product Reviews ◽  
Common Trend ◽  
Learning Techniques

With the increase in E-Commerce businesses in the last decade,the sentiment analysis of product reviews has gained a lot of attention in linguistic research. In literature, the survey depicts the majority of the research done emphasizes on mere polarity identification of the reviews. The proposed system emphasized on classifying the sentiment polarity and the product aspect identification from the reviews. Proposed work experimented with traditional machine learning techniques as well as deep neural networks such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory(LSTM) Networks. The proposed system gives a better understanding of these algorithms by comparing the outcomes. The Deep Learning approach in the proposed work successfully provides a mechanism which identifies the review polarity and intensity of the reviews and also analyses the short form words used by people in the reviews. The experimental results in this work, applied on amazon product dataset, shows that the LSTM model works the best for sentiment analysis and intensity of reviews with 93% accuracy. This research work also predicts polarity for short-form word reviews which is the common trend these days while writing the reviews.

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

scholarly journals Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1953
Author(s):  
Seyed Majid Azimi ◽  
Maximilian Kraus ◽  
Reza Bahmanyar ◽  
Peter Reinartz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Object Tracking ◽  
Short Term Memory ◽  
Aerial Imagery ◽  
Future Research ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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