Cooperative effects in surfactant adsorption layers at water/alkane interfaces

In the present work, the properties of dodecyl dimethyl phosphine oxide (C12DMPO) at the water/decane interface are studied and compared with those obtained earlier at the interface to hexane. To simulate the interfacial behavior, a two-component thermodynamic model is proposed, which combines the equation of state and Frumkin isotherm for decane with the reorientation model involving the intrinsic compressibility for the surfactant. In this approach, the surface activity of decane is governed by its interaction with C12DMPO. The theory predicts the influence of decane on the decrease of the surface tension at a very low surfactant concentration for realistic values of the ratio of the adsorbed amounts of decane and surfactant. The surfactantrsquo;s distribution coefficient between the aqueous and decane phases is determined. Two types of adsorption systems were used: a decane drop immersed into the C12DMPO aqueous solution, and a water drop immersed into the C12DMPO solution in decane. To determine the distribution coefficient, a method based on the analysis of the transfer of C12DMPO between water and decane is also employed

Svd-lda: Topic modeling for full-text recommender systems

© Springer International Publishing Switzerland 2015. In recommender systems, matrix decompositions, in particular singular value decomposition (SVD), represent users and items as vectors of features and allow for additional terms in the decomposition to account for other available information. In text mining, topic modeling, in particular latent Dirichlet allocation (LDA), are designed to extract topical content of a large corpus of documents. In this work, we present a unified SVD-LDA model that aims to improve SVD-based recommendations for items with textual content with topic modeling of this content. We develop a training algorithm for SVD-LDA based on a first order approximation to Gibbs sampling and show significant improvements in recommendation quality

Demographic prediction based on user reviews about medications

Drug reactions can be extracted from user reviews provided on the Web, and processing this information in an automated way represents a novel and exciting approach to personalized medicine and wide-scale drug tests. In medical applications, demographic information regarding the authors of these reviews such as age and gender is of primary importance; however, existing studies usually assume that this information is available or overlook the issue entirely. In this work, we propose and compare several approaches to automated mining of demographic information from user-generated texts. We compare modern natural language processing techniques, including feature rich classifiers, extensions of topic models, and deep neural networks (both convolutional and recurrent architectures) for this problem

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

© 2017 Elena Tutubalina and Sergey Nikolenko. Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction

Predicting the age of social network users from user-generated texts with word embeddings

© 2016 FRUCT.Many web-based applications such as advertising or recommender systems often critically depend on the demographic information, which may be unavailable for new or anonymous users. We study the problem of predicting demographic information based on user-generated texts on a Russian-language dataset from a large social network. We evaluate the efficiency of age prediction algorithms based on word2vec word embeddings and conduct a comprehensive experimental evaluation, comparing these algorithms with each other and with classical baseline approaches

Automated prediction of demographic information from medical user reviews

© 2017, Springer International Publishing AG.The advent of personalized medicine and wide-scale drug tests has led to the development of methods intended to automatically mine and extract information regarding drug reactions from user reviews. For medical purposes, it is often important to know demographic information on the authors of these reviews; however, existing studies usually either presuppose that this information is available or disregard the issue. We study automatic mining of demographic information from user-generated texts, comparing modern natural language processing techniques, including extensions of topic models and deep neural networks, for this problem on datasets mined from health-related web sites

Inferring sentiment-based priors in topic models

© Springer International Publishing Switzerland 2015. Over the recent years, several topic models have appeared that are specifically tailored for sentiment analysis, including the Joint Sentiment/Topic model, Aspect and Sentiment Unification Model, and User-Sentiment Topic Model. Most of these models incorporate sentiment knowledge in the β priors; however, these priors are usually set from a dictionary and completely rely on previous domain knowledge to identify positive and negative words. In this work, we show a new approach to automatically infer sentiment-based β priors in topic models for sentiment analysis and opinion mining; the approach is based on the EM algorithm. We show that this method leads to significant improvements for sentiment analysis in known topic models and also can be used to update sentiment dictionaries with new positive and negative words