A Deep Architecture for Sentiment Analysis of News Articles

Nguyen, Dinh Thanh; Vo, Khuong; Chinh, Pham Duc; Nguyen, Mao; Tho, Quan Thanh

doi:10.1007/978-3-319-61911-8_12

Cited by 17 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nguyen et al [ 50 ] proposed a novel method to detect polarity in news articles using a deep learning classifier. The authors used different websites to collect more than one million news articles and fed the preprocessed embedded vectors into CNN, LSTM and convolutional LSTM (CLSTM).…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

View full text Add to dashboard Cite

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Word embedding combined with multi-layer neural networks to handle various layers of semantic was suggested in Pham, Le, and Le (2016). In Vo, Nguyen, Le, and Nguyen (2017), a model combining CNN and LSTM, which is similar to our previous work (Nguyen et al, 2017), was proposed.…”

Section: Sentiment Analysis Approaches For Vietnamesementioning

confidence: 99%

“…The deep architecture for negative-oriented sentiment analysis 4.1. The previous architecture Figure 7 presents an overview of our previous work (Nguyen et al, 2017) of using deep architecture for sentiment analysis on news articles. The system includes the following modules.…”

Section: Sentiment Analysis Approaches For Vietnamesementioning

confidence: 99%

“…Whereas CNN is useful to extract hidden features from text data, LSTM is suitable to handle long sequence data. In our previous work (Nguyen et al, 2017), we proposed an architecture which combines CNN and LSTM to handle sentiment analysis of news articles on social media data. However, when applied with a huge amount of real mentions collected from social media channels, our architecture suffers from the following issues.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Handling negative mentions on social media channels using deep learning

Nguyen

Chinh

et al. 2019

Journal of Information and Telecommunication

Self Cite

View full text Add to dashboard Cite

Social media channels such as social networks, forum or online blogs have been emerging as major sources from which brands can gather user opinions about their products, especially the negative mentions. This kind of task, popular known as sentiment analysis, has been addressed recently by many deep learning approaches. However, negative mentions on social media have their own language characteristics which require certain adaptation and improvement from existing works for better performance. In this paper, we propose a new architecture for handling negative mentions on social media channels. As compared to the architecture published in our previous work, we expose substantial change in the combination manner of deep neural network layers for better training and classification performance on social-oriented messages. We also propose the way to retrain the pre-trained embedded words for better reflect sentiment terms, introducing the resultant sentimentally-embedded word vectors. Finally, we introduce the concept of a penalty matrix which incurs more reasonable loss function when handling negative mentions. Our experiments on real datasets demonstrated significant improvement.

show abstract

“…To store occurrence order relationship between features, recurrent neural network systems such as Long Short Term Memory (LSTM) was used in combination with convolution to perform sentiment analysis for short text [17]. Most recently, a combined architecture using deep learning for sentiment analysis has been proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis

Vo¹,

Chinh²,

Nguyen³

et al. 2017

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

The emerging technique of deep learning has been widely applied in many different areas. However, when adopted in a certain specific domain, this technique should be combined with domain knowledge to improve efficiency and accuracy. In particular, when analyzing the applications of deep learning in sentiment analysis, we found that the current approaches are suffering from the following drawbacks: (i) the existing works have not paid much attention to the importance of different types of sentiment terms, which is an important concept in this area; and (ii) the loss function currently employed does not well reflect the degree of error of sentiment misclassification. To overcome such problem, we propose to combine domain knowledge with deep learning. Our proposal includes using sentiment scores, learnt by quadratic programming, to augment training data; and introducing penalty matrix for enhancing the loss function of cross entropy. When experimented, we achieved a significant improvement in classification results.

show abstract

A Deep Architecture for Sentiment Analysis of News Articles

Cited by 17 publications

References 12 publications

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Handling negative mentions on social media channels using deep learning

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis

Contact Info

Product

Resources

About