Review Spam Detection Using Word Embeddings and Deep Neural Networks

Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. The efficacy and safety of new treatments for patients with these characteristics are, therefore, not defined. In this work, we built a model to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. We used protocols from cancer clinical trials that were available in public registries from the last 18 years to train word-embeddings, and we constructed a dataset of 6M short free-texts labeled as eligible or not eligible. A text classifier was trained using deep neural networks, with pre-trained word-embeddings as inputs, to predict whether or not short free-text statements describing clinical information were considered eligible. We additionally analyzed the semantic reasoning of the word-embedding representations obtained and were able to identify equivalent treatments for a type of tumor analogous with the drugs used to treat other tumors. We show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols for potentially assisting practitioners when prescribing treatments.

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Combining Word Embeddings and Deep Neural Networks for Job Offers and Resumes Classification in IT Recruitment Domain

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2021.0120774 ◽

2021 ◽

Vol 12 (7) ◽

Author(s):

Amine Habous ◽

El Habib Nfaoui

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Word Embeddings ◽

Job Offers

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Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings

10.20948/graphicon-2021-3027-1011-1016 ◽

2021 ◽

Author(s):

Ekaterina Popova ◽

Vladimir Spitsyn

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Social Networks ◽

Sentiment Analysis ◽

Deep Neural Networks ◽

Functional Method ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Word Embeddings ◽

Learning Techniques

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.

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