scholarly journals Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Buzhou Tang ◽  
Jianglu Hu ◽  
Xiaolong Wang ◽  
Qingcai Chen
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Medical Information ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Knowledge Bases ◽  
Short Term ◽  
External Knowledge ◽  
Long Short Term Memory ◽  
First Time

Social media in medicine, where patients can express their personal treatment experiences by personal computers and mobile devices, usually contains plenty of useful medical information, such as adverse drug reactions (ADRs); mining this useful medical information from social media has attracted more and more attention from researchers. In this study, we propose a deep neural network (called LSTM-CRF) combining long short-term memory (LSTM) neural networks (a type of recurrent neural networks) and conditional random fields (CRFs) to recognize ADR mentions from social media in medicine and investigate the effects of three factors on ADR mention recognition. The three factors are as follows: (1) representation for continuous and discontinuous ADR mentions: two novel representations, that is, “BIOHD” and “Multilabel,” are compared; (2) subject of posts: each post has a subject (i.e., drug here); and (3) external knowledge bases. Experiments conducted on a benchmark corpus, that is, CADEC, show that LSTM-CRF achieves better F-score than CRF; “Multilabel” is better in representing continuous and discontinuous ADR mentions than “BIOHD”; both subjects of comments and external knowledge bases are individually beneficial to ADR mention recognition. To the best of our knowledge, this is the first time to investigate deep neural networks to mine continuous and discontinuous ADRs from social media.

scholarly journals Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 235-239
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Arabic Language ◽  
Short Term ◽  
Term Memory ◽  
User Comments ◽  
Long Short Term Memory ◽  
Use Of Social Media

The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people’s comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.

scholarly journals Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 154984-154996
Author(s):  
Abdulmohsen Al-Thubaity ◽  
Atheer Alkhalifa ◽  
Abdulrahman Almuhareb ◽  
Waleed Alsanie
Keyword(s):  
Neural Networks ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Pengaruh Metode Penyeimbangan Kelas Terhadap Tingkat Akurasi Analisis Sentimen pada Tweets Berbahasa Indonesia

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Ivan Nathaniel Husada ◽  
Hapnes Toba
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Nowadays internet access is getting easier to get. Because of the ease of access to the internet, almost all internet users have social media. Social media is widely used by users to call out their opinions or even to make complaints about a matter and also discuss a topic with other social media users. From many existing social media, one that is popularly used for that activity is Twitter. Sentiment analysis on Twitter has become possible because of the activities of these Twitter users. In this research, the authors explore sentiment analysis with bag-of-words and Term Frequency Inverse Document Frequency (TF-IDF) features extraction based on tweets from Indonesian Twitter users. The data obtained is in imbalanced condition, so that it requires a method to overcome them. The method for overcoming imbalanced dataset uses a resampling approach which combines over and under sampling strategies. The results of sentiment analysis accuracies with Naïve Bayes and neural networks before and after input data resampling are also compared. Naïve Bayes methods that will be used are Multinomial Naïve Bayes and Complement Naïve Bayes, while the Neural Network architecture that will be used as a comparison are Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Units, Convolutional Neural Networks, and a combination of Convolutional Neural Networks and Long Short-Term Memory. Our experiments show the following harmonic scores (F1) of the sentiment analysis models: the Multinomial Naïve Bayes F1 score is 55.48, Complement Naïve Bayes is 51.33, Recurrent Neural Network  is 75.70, Long Short-Term Memory is 78.36, Gated Recurrent Unit is 77.96, Convolutional Neural Network is 76.12, and finally the combination of Convolutional Neural Networks and Long Short-Term Memory achieves 81.14.

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