News Text Classification Based on Improved Bi-LSTM-CNN

Li, Chenbin; Zhan, Guohua; Li, Zhihua

doi:10.1109/itme.2018.00199

Cited by 105 publications

(34 citation statements)

References 2 publications

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“…. The calculation process of LSTM can be summarized as follows [16]: by forgetting the information in the cell state and memorizing new information, the useful information for the subsequent calculation can be transmitted, while the useless information is discarded, and the hidden state t h will be output at each step, in which the forgetting, memory and output are calculated by the hidden state 1 t h − of the previous time and the current input t X , the forgetting gate t f , the memory gate t i and the output gate t O to control [18] [19]. Forgetting gate is used to select the information to be forgotten.…”

Section: Emotion Analysis Based On Bi-lstm Modelmentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

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Section: Emotion Analysis Based On Bi-lstm Modelmentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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“…Although most of the studies in the literature are applied for classifying English news (e.g., [70][71][72]), some researches have recently enriched the related literature by focusing on other languages, employing machine learning techniques (e.g., [73][74][75][76][77]).…”

Section: A Brief Review Of the Related Literaturementioning

confidence: 99%

News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

et al. 2020

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‘El Diario de Juárez’ is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service. This research applies natural-language-processing and machine-learning algorithms to the news provided by the RSS service in order to classify them based on whether they are about a traffic incident or not, with the final intention of notifying citizens where such accidents occur. The classification process explores the bag-of-words technique with five learners (Classification and Regression Tree (CART), Naïve Bayes, kNN, Random Forest, and Support Vector Machine (SVM)) on a class-imbalanced benchmark; this challenging issue is dealt with via five sampling algorithms: synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, random oversampling, and random undersampling. Consequently, our final classifier reaches a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is an acceptable performance when considering the complexity of analyzing unstructured texts in Spanish.

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“…The main differences refer, in essence, to the presence/absence of different gates or to the input of each one. Due to its characteristics, LSTMs have achieved remarkable results in sequence problems such as text classification (Breuel, 2017;Chenbin et al, 2018), music generation (Coca et al, 2013;Choi et al, 2016), handwriting recognition (Pham et al, 2014;Messina and Louradour, 2015) or speech recognition (Graves et al, 2013;Sak et al, 2014), just to name a few. Traffic forecasting is yet another domain where LSTMs have been applied successfully (Tian and Pan, 2015;Fu et al, 2016;Cui et al, 2018).…”

Section: Arima Models and Lstm Networkmentioning

confidence: 99%

Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Fernandes¹,

Silva²,

Aláiz‐Moretón³

et al. 2020

Informatica

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Traffic flow forecasting is an acknowledged time series problem whose solutions have been essentially grounded on statistical-based models. Recent times came, however, with promising results regarding the use of Recurrent Neural Networks (RNNs), such as Long Short-Term Memory networks (LSTMs), to accurately address time series problems. Literature is, however, evasive in regard to several aspects of the conceived models and often exhibits misconceptions that may lead to important pitfalls. This study aims to conceive and find the best possible LSTM model for traffic flow forecasting while addressing several important aspects of such models such as the multitude of input features, the time frames used by the model and the employed approach for multi-step forecasting. To overcome the spatial problem of open source datasets, this study presents and describes a new dataset collected by the authors of this work. After several weeks of model fitting, Recursive Multi-Step Multi-Variate models were the ones showing better performance, strengthening the perception that LSTMs can be used to accurately forecast the traffic flow for several future timesteps.

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News Text Classification Based on Improved Bi-LSTM-CNN

Cited by 105 publications

References 2 publications

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

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