Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

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Fake news in social media recognition using Modified Long Short-Term Memory network

Security in IoT Social Networks ◽

10.1016/b978-0-12-821599-9.00009-1 ◽

2021 ◽

pp. 205-227

Author(s):

Sivaranjani Reddi ◽

G.V. Eswar

Keyword(s):

Social Media ◽

Short Term Memory ◽

Fake News ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory

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Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Sustainability ◽

10.3390/su12177076 ◽

2020 ◽

Vol 12 (17) ◽

pp. 7076 ◽

Cited By ~ 3

Author(s):

Arash Moradzadeh ◽

Sahar Zakeri ◽

Maryam Shoaran ◽

Behnam Mohammadi-Ivatloo ◽

Fazel Mohammadi

Keyword(s):

Support Vector Regression ◽

Performance Metrics ◽

Short Term Memory ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Short Term Load Forecasting ◽

Long Short Term Memory ◽

Input Variables

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

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Drastic Fluctuation Prediction of the Number of Comments on Social Media by Attributes of Comments

International Journal of Semantic Computing ◽

10.1142/s1793351x2150001x ◽

2021 ◽

Vol 15 (01) ◽

pp. 1-21

Author(s):

Yuan Wang ◽

Guan-Shen Fang ◽

Sayaka Kamei

Keyword(s):

Social Media ◽

Short Term Memory ◽

Real Data ◽

Web Content ◽

Short Term ◽

Social Responses ◽

Term Memory ◽

Online Social Media ◽

Relevant Topic ◽

Long Short Term Memory

Online social media has an exponential level of communication speed in terms of message dissemination. Users can publish comments freely to various web content on a characteristic network of communicators and viewers. Many of these comments contain emotions or opinions of users, which may cause sympathy and influence others’ comments. Moreover, such comments may raise social responses, i.e. they may cause drastic fluctuations in the number of comments. In this study, using the content of textual comments, we propose two structural approaches (PDFCPL and PDFCML) to predict the future drastic fluctuation in the number of comments based on Long Short-Term Memory (LSTM). To quantify each textual comment, we define two attributes: (1) relevance to its relevant topic based on cosine similarity and (2) importance of its content which is calculated by TF-IDF. The predictions are made by these attributes and the number of previously observed comments as well. To evaluate the performance of our approaches, we conduct comparing experiments with other methods on real data of Twitter. The results present that the proposed method PDFCPL has better performance than existing methods to predict the occurrence of drastic fluctuation in the number of comments.

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A semiautomatic annotation approach for sentiment analysis

Journal of Information Science ◽

10.1177/01655515211006594 ◽

2021 ◽

pp. 016555152110065

Author(s):

Rahma Alahmary ◽

Hmood Al-Dossari

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Annotation Process ◽

Learning Classifiers ◽

Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

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Multi-Step-Ahead Prediction with Long Short Term Memory Networks and Support Vector Regression

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8484066 ◽

2018 ◽

Cited By ~ 3

Author(s):

Sai Li ◽

Huajing Fang ◽

Bing Shi

Keyword(s):

Support Vector Regression ◽

Short Term Memory ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

International Journal of Integrated Engineering ◽

10.30880/ijie.2021.13.04.007 ◽

2021 ◽

Vol 13 (4) ◽

Author(s):

Ralph Sherwin A. Corpuz ◽

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Natural Language ◽

Text Categorization ◽

Short Term Memory ◽

Support Vector ◽

Feature Engineering ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications.

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Short-Term Building Load Forecast Based on Patch Learning with Long Short-Term Memory Network and Support Vector Regression

10.1007/978-981-16-5188-5_53 ◽

2021 ◽

pp. 742-756

Author(s):

Zhaohui Dan ◽

Bo Wang ◽

Huijin Fan ◽

Lei Liu

Keyword(s):

Support Vector Regression ◽

Short Term Memory ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Load Forecast ◽

Memory Network ◽

Building Load ◽

Long Short Term Memory

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Ensemble-Based Feature Selection With Long Short-Term Memory for Classification of Network Intrusion

Advances in Social Networking and Online Communities - E-Collaboration Technologies and Strategies for Competitive Advantage Amid Challenging Times ◽

10.4018/978-1-7998-7764-6.ch008 ◽

2021 ◽

pp. 228-245

Author(s):

Preethi D. ◽

Neelu Khare

Keyword(s):

Feature Selection ◽

Performance Metrics ◽

Short Term Memory ◽

Short Term ◽

Chi Square ◽

Term Memory ◽

Network Intrusion ◽

Proposed Model ◽

Long Short Term Memory

This chapter presents an ensemble-based feature selection with long short-term memory (LSTM) model. A deep recurrent learning model is proposed for classifying network intrusion. This model uses ensemble-based feature selection (EFS) for selecting the appropriate features from the dataset and long short-term memory for the classification of network intrusions. The EFS combines five feature selection techniques, namely information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the standard benchmark NSL-KDD dataset and implemented using tensor flow and python. The proposed model is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection technique and classified using LSTM. The performance study showed that the proposed model performs better, with 99.8% accuracy, with a higher detection and lower false alarm rates.

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