#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter

Mahata, Debanjan; Friedrichs, Jasper; Hitkul,; Shah, Rajiv Ratn

doi:10.48550/arxiv.1805.06375

Cited by 3 publications

(3 citation statements)

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“…Among the classic machine learning techniques, Support Vector Machine (SVM) with a linear kernel (Hsu et al, 2003), Logistic Regression (Yu et al, 2011) and Random Forests (Breiman, 2001) were trained. A shallow Convolutional Neural Network with a single input channel similar to (Severyn and Moschitti, 2015), and Bidirectional Long Short Term Memory networks with an architecture similar to (Mahata et al, 2018), are the deep learning models that were trained. A random classifier that randomly generated predictions from a label distribution similar to that of the training dataset was also implemented.…”

Section: Trainingmentioning

confidence: 99%

BHAAV (भाव) - A Text Corpus for Emotion Analysis from Hindi Stories

Kumar

Mahata²,

aggarwal³

et al. 2019

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In this paper, we introduce the first and largest Hindi text corpus, named BHAAV (भाव) , which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as े रणादायक (Inspirational) and रह यमयी (Mystery). Each sentence has been annotated into one of the five emotion categories (anger, joy, suspense, sad, and neutral), by three native Hindi speakers with at least ten years of formal education in Hindi. We also discuss challenges in the annotation of low resource languages such as Hindi, and discuss the scope of the proposed corpus along with its possible uses. We also provide a detailed analysis of the dataset and train strong baseline classifiers reporting their performances.

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Section: Trainingmentioning

confidence: 99%

BHAAV (भाव) - A Text Corpus for Emotion Analysis from Hindi Stories

Kumar

Mahata²,

aggarwal³

et al. 2019

Self Cite

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“…Due to immense success of Convolutional Neural Network (CNN) for challenging tasks of classification, object detection, segmentation, etc., it has now gained widespread popularity [3,14]. Since not many attempts have been made for popularity prediction of photos by using CNNs, we explored their use for the continuous output regression tasks like prediction.…”

Section: Related Workmentioning

confidence: 99%

A Multimodal Approach to Predict Social Media Popularity

Meghawat¹,

Yadav²,

Mahata³

et al. 2018

Preprint

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Multiple modalities represent different aspects by which information is conveyed by a data source. Modern day social media platforms are one of the primary sources of multimodal data, where users use different modes of expression by posting textual as well as multimedia content such as images and videos for sharing information. Multimodal information embedded in such posts could be useful in predicting their popularity. To the best of our knowledge, no such multimodal dataset exists for the prediction of social media photos. In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction. Specifically, we augment the SMP-T1 dataset for social media prediction in ACM Multimedia grand challenge 2017 with image content, titles, descriptions, and tags. Next, in this paper, we propose a multimodal approach which exploits visual features (i.e., content information), textual features (i.e., contextual information), and social features (e.g., average views and group counts) to predict popularity of social media photos in terms of view counts. Experimental results confirm that despite our multimodal approach uses the half of the training dataset from SMP-T1, it achieves comparable performance with that of state-of-the-art.

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“…The best traditional classification method proposed by NRC-Canada implements a support vector machine classifier using a variety of surface-form, sentiment, and domain-specific features [Kiritchenko et al 2018]. InfyNLP explores the applicability of various deep learning models and concludes that a stacked ensemble of shallow CNN performs relatively better when hyperparameters of the model are fine-tuned [Mahata et al 2018]. This model was ranked first in the SMM4H 2017 shared task.…”

Section: Introductionmentioning

confidence: 99%

Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations

Jarashanth¹,

Nawarathna²

2022

jucs

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidirectional gated recurrent units (BiGRU), and context-aware attentions. An embedding for a word is produced by integrating both word-level and character-level embeddings using an embedding-level attention mechanism, which selects the salient features from both embeddings without expanding dimensionality. The resultant embedding is further analyzed by three CNN layers independently, where each extracts unique n-grams. BiGRUs followed by attention layers further process the outputs from each CNN layer. Besides, the resultant embedding is also encoded by a BiGRU with attention. Our model is developed to cope with the intricate attributes inherent to tweets such as vernacular texts, descriptive medical phrases, frequently misspelt words, abbreviations, short messages, and others. All these four outputs are summed and sent to a softmax classifier. We built a dataset by incorporating tweets from two benchmark datasets designed for the same objective to evaluate the performance. Our model performs substantially better than existing models, including several customized Bidirectional Encoder Representations from Transformers (BERT) models with an F1-score of 0.772.

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#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter

Cited by 3 publications

References 0 publications

BHAAV (भाव) - A Text Corpus for Emotion Analysis from Hindi Stories

BHAAV (भाव) - A Text Corpus for Emotion Analysis from Hindi Stories

A Multimodal Approach to Predict Social Media Popularity

Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations

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