Identifying product failures from reviews in noisy data by distant supervision

© Springer International Publishing Switzerland 2016.Product reviews contain valuable information regarding customer satisfaction with products. Analysis of a large number of user requirements attracts interest of researchers. We present a comparative study of distantly supervised methods for extraction of user complaints from product reviews. We investigate the use of noisy labeled data for training classifiers and extracting scores for an automatically created lexicon to extract features. Several methods for label assignment were evaluated including keywords, syntactic patterns, and weakly supervised topic models. Experimental results using two real-world review datasets about automobiles and mobile applications show that distantly supervised classifiers outperform strong baselines

Mining complaints to improve a product: A study about problem phrase extraction from user reviews

© 2016 Copyright held by the owner/author(s).The rapidly growing availability of user reviews has become an important resource for companies to detect customer dissatisfaction from textual opinions. There have been few recent studies conducted on business-related opinion tasks to extract more refined opinions about a product's quality problems or technical failures. The focus of this study is the extraction of problem phrases, mentioned in user reviews about products. We explore main opinion mining tasks to determine whether given text from reviews contains a mention of a problem. We formulate research questions and propose knowledge-based methods and probabilistic models to classify users' phrases and extract latent problem indicators, aspects and related sentiments from online reviews

Target-based topic model for problem phrase extraction

© Springer International Publishing Switzerland 2015. Discovering problems from reviews can give a company a precise view on strong and weak points of products. In this paper we present a probabilistic graphical model which aims to extract problem words and product targets from online reviews. The model extends standard LDA to discover both problem words and targets. The proposed model has two conditionally independent variables and learns two distributions over targets and over text indicators, associated with both problem labels and topics. The algorithm achieves a better performance in comparison to standard LDA in terms of the likelihood of a held-out test set

A sentiment-aware topic model for extracting failures from product reviews

© Springer International Publishing Switzerland 2016.This paper describes a probabilistic model that aims to extract different kinds of product difficulties conditioned on users’ dissatisfaction through the use of sentiment information. The proposed model learns a distribution over words, associated with topics, sentiment and problem labels. The results were evaluated on reviews of products, randomly sampled from several domains (automobiles, home tools, electronics, and baby products), and user comments about mobile applications, in English and Russian. The model obtains a better performance than several state-of-the-art models in terms of the likelihood of a held-out test and outperforms these models in a classification task

TEXT MINING IN BIOMEDICAL RESEARCH

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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

Clause-based approach to extracting problem phrases from user reviews of products

©Springer International Publishing Switzerland 2014. This paper describes approaches to problem-phrase extraction from user reviews of products. The first step in problem extraction is to separate sentences with problems from all others. We propose two methods to problem extraction from such sentences: (i) a straightforward algorithm that does not split sentence into clauses and (ii) an improved clause-based algorithm.We claim that both approaches improve the classification performance compared to machine-learning algorithms

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

KFU at CLEF eHealth 2017 Task 1: ICD-10 coding of English death certificates with recurrent neural networks

This paper describes the participation of the KFU team in the CLEF eHealth 2017 challenge. Specifically, we participated in Task 1, namely "Multilingual Information Extraction - ICD-10 coding" for which we implemented recurrent neural networks to automatically assign ICD-10 codes to fragments of death certificates written in English. Our system uses Long Short-Term Memory (LSTM) to map the input sequence into a vector representation, and then another LSTM to decode the target sequence from the vector. We initialize the input representations with word embeddings trained on user posts in social media. The encoderdecoder model obtained F-measure of 85.01% on a full test set with significant improvement as compared to the average score of 62.2% for all participants' approaches. We also obtained significant improvement from 26.1% to 44.33% on an external test set as compared to the average score of the submitted runs

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