Fake news identification on Twitter with hybrid CNN and RNN models

The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain

Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions

Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translation research. In recent years, deep learning methods have achieved significant success in CNER tasks. However, these methods depend greatly on Recurrent Neural Networks (RNNs), which maintain a vector of hidden activations that are propagated through time, thus causing too much time to train models. In this paper, we propose a Residual Dilated Convolutional Neural Network with Conditional Random Field (RD-CNN-CRF) to solve it. Specifically, Chinese characters and dictionary features are first projected into dense vector representations, then they are fed into the residual dilated convolutional neural network to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. Computational results on the CCKS-2017 Task 2 benchmark dataset show that our proposed RD-CNN-CRF method competes favorably with state-of-the-art RNN-based methods both in terms of computational performance and training time.Comment: 8 pages, 3 figures. Accepted as regular paper by 2018 IEEE International Conference on Bioinformatics and Biomedicine. arXiv admin note: text overlap with arXiv:1804.0501

Using convolution neural networks for improving customer requirements classification performance of autonomous vehicle

Customer requirements are vital information prior to the early stage of autonomous vehicle (AV) development processes. In the development process of AV many decisions have been made concerning customer requirements at the first stage. The development of AV that meets customer requirements will increase the global consumer and remain competitive. Safety and regulation are one of crucial aspect for customers that requires to be concerned and evaluated at the early stage of AV development. If safety and regulation related requirements did not well identified, AV developer could not develop the safest vehicles due to the huge compensation of accidents. To efficiently classify customer requirements, this study proposed an approach based on natural language processing method. For classification purpose, the customer requirements are divided into six categories that the concept are come from the quality management system (QMS) standard. These categories will be as input for the next process development in making the best decision. Most of conventional algorithms, such as, Naive Bayes, MAXENT, and support vector machine (SVM), only use limited human engineered features and their accuracy for customized corpus in sentences classification are proven low which is less than 50 percent. However, in literature, convolution neural networks (CNN) have been described efficiently to overcome the customized corpus of sentence classification problems. Therefore, this study implements CNN architecture in customized corpus classification operations. As the results, the accuracy of CNN classification has improved at least 6 percent compared to the conventional algorithms

Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM-CNN model is evaluated on standard dataset on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN-LSTM model. The paper also shows that the LSTM-CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction

Detección automática de tweets noticiosos

Treball final de Màster Universitari en Sistemes Intel.ligents (Pla de 2013). Codi: SIE043. Curs acadèmic 2018/2019Las redes sociales, como Twitter, pueden facilitar la distribución de información en tiempo real entre usuarios de todo el mundo. Se ha demostrado que las personas cada vez reciben más noticias de las redes sociales que de las fuentes de noticias tradicionales. El objetivo del presente trabajo es la detección automática de tweets noticiosos. Se ha realizado, primeramente, un etiquetado de tweets noticiosos y un conjunto de no etiquetado, en el cual se encuentran los negativos. Para el proceso de extracción de negativos desde el conjunto de no etiquetados se realiza un proceso de PU-Learning. Además, por desbalanceo del conjunto se desarrolla una aumentación de datos, mediante la creación de tweets sintéticos. Finalmente, se propone un modelo, con aproximaciones de aprendizaje profundo, para la detección de tweets con los conjuntos obtenidos por el PU-Learning. Este modelo alcanza como resultado un 0.86 de F1-score y precisión de 0.98.Social networks, such as Twitter, can facilitate the distribution of information in real time among users around the world. The people receive more news from social networks than from traditional news sources. The objective of this work is the automatic detection of newsworthy tweets. A labelling of newsworthy tweets and unlabeled was done. For the process of extracting negatives from the set of unlabeled, a PU-Learning process is applied. In addition, due to the imbalance of set, a data augmentation has been developed, through the creation of synthetic tweets. Finally, a model has been proposed, with deep learning approaches, for the detection of tweets with the sets detected by the PU-Learning. This model results in a 0.86 F1-score and precision of 0.98

PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.Comment: 21 pages, submitted preprint to Elsevier Expert Systems with Application