Spam detection in social media using convolutional and long short term memory neural network

2019 ◽  
Vol 85 (1) ◽  
pp. 21-44 ◽  
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Short Term Memory ◽  
Spam Detection ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Comparative Analysis of Deep Learning Techniques for the Classification of Hate Speech

2021 ◽  
Vol 4 (1) ◽  
pp. 121-128
Author(s):  
A Iorliam ◽  
S Agber ◽  
MP Dzungwe ◽  
DK Kwaghtyo ◽  
S Bum
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Or Groups ◽  
Long Short Term Memory

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.

scholarly journals Implementasi Algoritma Long Short-Term Memory (LSTM) Untuk Mendeteksi Ujaran Kebencian (Hate Speech) Pada Kasus Pilpres 2019

2019 ◽  
Vol 19 (1) ◽  
pp. 37-44
Author(s):  
Aini Suri Talita ◽  
Aristiawan Wiguna
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Hate Speech ◽  
Short Term Memory ◽  
Model Performance ◽  
Literature Study ◽  
Short Term ◽  
Term Memory ◽  
Testing Data ◽  
Long Short Term Memory

Researches involving Artificial Neural Network (ANN) or its derivative have been published all around the world, spesifically to solve data mining problem, classification, clusterinf, or detection problems. Recurrent Neural Network is a class of ANN with Long Short Term Memory (LSTM) as its one of the architecture that commonly used in deep learning problems. On this paper, we use LSTM to detect hate speech on social media related with Indonesia President Election on 2019. There are several steps on this research, we start with literature study, data collection, data preprocessing, training step, and testing step.  The dataset consist of 950 sentences, while the testing data consist of 190 comments on Facebook. The best model performance was reached with recall value 0.7021, which menas that from the whole relevant instances on the testing data, 70.21% were categorized as relevant, on this case as hate speech (HS). The other performance parameter value as in accuracy and precision still quite low due to the testing data that comes directly from social media which highly possible consist of inconsistent choises of words, informal words, or contains grammatically error sentences.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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