scholarly journals Fake News Detection Using NLP

2021 ◽  
Vol 9 (12) ◽  
pp. 2022-2031
Author(s):  
Samrudhi Naik
Keyword(s):  
Language Processing ◽  
Good Accuracy ◽  
Short Term Memory ◽  
Fake News ◽  
Short Term ◽  
Traditional Media ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Media Channels ◽  
Better Than

Abstract: The spreading of fake news has given rise to many problems in society. It is due to its ability to cause a lot of social and national damage with destructive impacts. Sometimes it gets very difficult to know if the news is genuine or fake. Therefore it is very important to detect if the news is fake or not. "Fake News" is a term used to represent fabricated news or propaganda comprising misinformation communicated through traditional media channels like print, and television as well as nontraditional media channels like social media. Techniques of NLP and Machine learning can be used to create models which can help to detect fake news. In this paper we have presented six LSTM models using the techniques of NLP and ML. The datasets in comma-separated values format, pertaining to political domain were used in the project. The different attributes like the title and text of the news headline/article were used to perform the fake news detection. The results showed that the proposed solution performs well in terms of providing an output with good accuracy, precision and recall. The performance analysis made between all the models showed that the models which have used GloVe and Word2vec method work better than the models using TF-IDF. Further, a larger dataset for better output and also other factors such as the author ,publisher of the news can be used to determine the credibility of the news. Also, further research can also be done on images, videos, images containing text which can help in improving the models in future. Keywords: Fake news detection, LSTM(long short term memory),Word2Vec,TF-IDF,Natural Language Processing.

scholarly journals Chinese Text Classification Model Based on Deep Learning

2018 ◽  
Vol 10 (11) ◽  
pp. 113 ◽  
Author(s):  
Yue Li ◽  
Xutao Wang ◽  
Pengjian Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Chinese Text ◽  
Text Classification ◽  
Short Term Memory ◽  
Classification Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

scholarly journals An Optimized Abstractive Text Summarization Model Using Peephole Convolutional LSTM

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1290 ◽  
Author(s):  
Rahman ◽  
Siddiqui
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
State Of The Art ◽  
Text Summarization ◽  
Short Term ◽  
Term Memory ◽  
Semantic Coherence ◽  
Long Short Term Memory ◽  
Central Composite ◽  
Convolutional Lstm

Abstractive text summarization that generates a summary by paraphrasing a long text remains an open significant problem for natural language processing. In this paper, we present an abstractive text summarization model, multi-layered attentional peephole convolutional LSTM (long short-term memory) (MAPCoL) that automatically generates a summary from a long text. We optimize parameters of MAPCoL using central composite design (CCD) in combination with the response surface methodology (RSM), which gives the highest accuracy in terms of summary generation. We record the accuracy of our model (MAPCoL) on a CNN/DailyMail dataset. We perform a comparative analysis of the accuracy of MAPCoL with that of the state-of-the-art models in different experimental settings. The MAPCoL also outperforms the traditional LSTM-based models in respect of semantic coherence in the output summary.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

scholarly journals COVID-19 ChatBot

2021 ◽  
Vol 9 (11) ◽  
pp. 44-49
Author(s):  
Satish Tirumalapudi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
And Control ◽  
Prediction Problems

Abstract: Chat bots are software applications that help users to communicate with the machine and get the required result, this is where Natural Language Processing (NLP) comes into the picture. Natural language processing is based on deep learning that enables computers to acquire meaning from inputs given by the users. Natural language processing techniques can make possible the use of natural language to express ideas, thus drastically increasing accessibility. NLP engines rely on the elements of intent, utterance, entity, context, and session. Here in this project, we will be using Deep learning techniques which will be trained on the dataset which contains categories, patterns, and responses. Long Short-Term Memory (LSTM) is a Recurrent Neural Network that is capable of learning order dependence in sequence prediction problems. One of the most popular RNN approaches is LSTM to identify and control a dynamic system. We use an RNN to classify the category user’s message belongs to and then will give a response from the list of responses. Keywords: NLP – Natural Language Processing, LSTM – Long Short Term Memory, RNN – Recurrent Neural Networks.

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

2021 ◽  
Vol 16 (6) ◽  
pp. 1-18
Author(s):  
T. V. Divya ◽  
Barnali Gupta Banik
Keyword(s):  
Social Media ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Word Embedding ◽  
Support Vector ◽  
Fake News ◽  
Short Term ◽  
Term Memory ◽  
Online Social Media ◽  
Long Short Term Memory

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. .

scholarly journals Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia

2021 ◽  
Vol 10 (4) ◽  
pp. 354-361
Author(s):  
Yudi Widhiyasana ◽  
Transmissia Semiawan ◽  
Ilham Gibran Achmad Mudzakir ◽  
Muhammad Randi Noor
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Rate ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.

scholarly journals Learning molecular dynamics with simple language model built upon long short-term memory neural network

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sun-Ting Tsai ◽  
En-Jui Kuo ◽  
Pratyush Tiwary
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Language Model ◽  
Short Term ◽  
Term Memory ◽  
Molecular Systems ◽  
Simple Language ◽  
Long Short Term Memory

Abstract Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model’s reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems.

scholarly journals An efficient sentiment analysis methodology based on long short-term memory networks

2021 ◽  
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Short Term ◽  
Good Decision ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Current Research Interest

AbstractSentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

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