Learning a statistical model of product aspects for sentiment analysis

En este art culo se introduce una nueva metodolog a para modelar ca- racter sticas de productos a partir de una colecci on de opiniones de usuarios. La metodolog a propuesta se basa en modelos estad sticos de lenguajes y es aplicable a productos de dominio arbitrario. La metodolog a combina un kernel de palabras de opini on con un modelo de traducci on de palabras para estimar el modelo de caracter sticas. Se presenta adem as un m etodo para modelar las opiniones vertidas sobre las caracter sticas. Los experimentos realizados sobre diferentes colecciones de opiniones muestran resultados alentadores en el modelado tanto de caracter sticas como de opiniones vertidas sobre estasIn this paper, we introduce a new methodology for modeling product aspects from a collection of free-text customer reviews. The proposal relies on a lan- guage modeling framework and is domain independent. It combines both a kernel- based model of opinion words and a stochastic translation model between words to approach the aspect model of products. We also present a ranking-based met- hodology to model the sentiments expressed about the aspects. The experiments carried out over several collections of customer reviews show encouraging results in the modeling of product aspects and their sentiments even from individual customer review

Aprendizaje de un modelo de características de productos para el análisis de opiniones

En este artículo se introduce una nueva metodología para modelar características de productos a partir de una colección de opiniones de usuarios. La metodología propuesta se basa en modelos estadísticos de lenguajes y es aplicable a productos de dominio arbitrario. La metodología combina un kernel de palabras de opinión con un modelo de traducción de palabras para estimar el modelo de características. Se presenta además un método para modelar las opiniones vertidas sobre las características. Los experimentos realizados sobre diferentes colecciones de opiniones muestran resultados alentadores en el modelado tanto de características como de opiniones vertidas sobre éstas.In this paper, we introduce a new methodology for modeling product aspects from a collection of free-text customer reviews. The proposal relies on a language modeling framework and is domain independent. It combines both a kernel-based model of opinion words and a stochastic translation model between words to approach the aspect model of products. We also present a ranking-based methodology to model the sentiments expressed about the aspects. The experiments carried out over several collections of customer reviews show encouraging results in the modeling of product aspects and their sentiments even from individual customer reviews.This work has been partially funded by the “Ministerio de Economía y Competitividad” with contract number TIN2011-24147 and by the Fundació Caixa Castelló project P1-1B2010-49. Lisette García-Moya has been supported by the PhD Fellowship Program of the Universitat Jaume I (PREDOC/2009/12)

Modeling and analyzing opinions from customer reviews

The main motivation behind this thesis is the problem of aspect-based sentiment summarization and its application to Business Intelligence (BI). Given a collection of opinion posts, aspect-based summarization has to do with extracting from the collection the most relevant opined aspects (also called features) along with their associated sentiment information (usually an opinion word and/or a polarity score that express the sentiment orientation of the opinion). In the recent scenario of e-commerce, we presume that BI could rely on extracted knowledge from reviews available in the Web in order to analyze recent trends as well as the satisfaction and behavior of customers and to prepare strategic plans accordingly. Specifically, this thesis proposes new methodologies to: - model and extract the opinions and their respective targets (i.e., aspects or features) from collections of opinion posts, and - integrate the extracted sentiment data into a traditional corporate data warehouse to enable BI. The modeling of opinions and their targets takes place in the general framework of statistical language modeling. The hypothesis is that there exists a language model of opinion words able to model the opinion lexicon of a domain, and that there is also a language model of aspects that can be learned from the model of opinions. Both the learning of the models and the extraction of the sentiment data (i.e., the tuples feature-opinion) are implemented using unsupervised approaches that do not need exhaustive natural language processing (except for POS-tagging/ lemmatization). The resulting methodologies can be applied to any language and domain given a seed set of general-domain opinion words. For the integration of sentiment data with traditional corporate data two scenarios are considered: a static one in which both the data sources and the user requirements are static and known in advance, and dynamic one based on an open data infrastructure where BI data can be linked to external sources on demand, without being attached to predefined (rigid) data structures or multidimensional schemas. We demonstrate our proposal on datasets of real opinions available in the Web. Results of the proposed method corroborate the thesis claims and show a good effectivity for their usage as a BI analysis tool

Retrieving Product Features and Opinions from Customer Reviews

A new methodology based on language models retrieves product features and opinions from a collection of free-text customer reviews about a product or service. The proposal relies on a language-modeling framework that can be applied to reviews in any domain and language provided with a minimal knowledge source of sentiments or opinions

Combining Probabilistic Language Models for Aspect-Based Sentiment Retrieval

In this paper, we present a new methodology aimed at retrieving relevant product aspects from a collection of customer reviews, as well as the most salient sentiments expressed about them. Our proposal is both unsupervised and domain independent, and does not relies on NLP techniques such as parsing or dependence analysis. In our experiments, the proposed method achieves good values of precision. It is also shown that our approach is capable of properly retrieving the relevant aspects and their sentiments even from individual reviews

Integrating Web Feed Opinions into a Corporate Data Warehouse

Web opinion feeds have become one of the most popular information sources users consult before buying products or contracting services. Negative opinions about some product can have a high impact in its sales figures. As a consequence, companies are more and more concerned about how to integrate this information in their Business Intelligence (BI) models so that they can predict sales figures or define new strategic goals. In this paper, we present an approach to integrate sentiment data extracted from web feeds into the corporate warehouse where company analytical data and models are stored. Such an integration allows users to perform new analysis tasks by using the traditional OLAP-based data warehouse operators. We have developed a case study over a set of real opinions about digital devices which are offered by a wholesaler company. Over this case study, the quality of the extracted sentiment data is evaluated, and some query examples that illustrate the potential uses of the integrated model are presented. 1

SLOD-BI: An Open Data Infrastructure for Enabling Social Business Intelligence

The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of a high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention to the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial to put in context the results of their analyses. Recent advances in Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper deals with these issues and proposes a novel semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI. This infrastructure follows the principles of the Linked Open Data initiative“Ministerio de Economía y Competitividad” (Spain) with contract number TIN2011-24147 and TIN2014-55335-

Storing and analysing voice of the market data in the corporate data warehouse

Web opinion feeds have become one of the most popular information sources users consult before buying products or contracting services. Negative opinions about a product can have a high impact in its sales figures. As a consequence, companies are more and more concerned about how to integrate opinion data in their business intelligence models so that they can predict sales figures or define new strategic goals. After analysing the requirements of this new application, this paper proposes a multidimensional data model to integrate sentiment data extracted from opinion posts in a traditional corporate data warehouse. Then, a new sentiment data extraction method that applies semantic annotation as a means to facilitate the integration of both types of data is presented. In this method, Wikipedia is used as the main knowledge resource, together with some well-known lexicons of opinion words and other corporate data and metadata stores describing the company products like, for example, technical specifications and user manuals. The resulting information system allows users to perform new analysis tasks by using the traditional OLAP-based data warehouse operators. We have developed a case study over a set of real opinions about digital devices which are offered by a wholesale dealer. Over this case study, the quality of the extracted sentiment data is evaluated, and some query examples that illustrate the potential uses of the integrated model are provided

Ticagrelor in patients with diabetes and stable coronary artery disease with a history of previous percutaneous coronary intervention (THEMIS-PCI) : a phase 3, placebo-controlled, randomised trial

Background: Patients with stable coronary artery disease and diabetes with previous percutaneous coronary intervention (PCI), particularly those with previous stenting, are at high risk of ischaemic events. These patients are generally treated with aspirin. In this trial, we aimed to investigate if these patients would benefit from treatment with aspirin plus ticagrelor. Methods: The Effect of Ticagrelor on Health Outcomes in diabEtes Mellitus patients Intervention Study (THEMIS) was a phase 3 randomised, double-blinded, placebo-controlled trial, done in 1315 sites in 42 countries. Patients were eligible if 50 years or older, with type 2 diabetes, receiving anti-hyperglycaemic drugs for at least 6 months, with stable coronary artery disease, and one of three other mutually non-exclusive criteria: a history of previous PCI or of coronary artery bypass grafting, or documentation of angiographic stenosis of 50% or more in at least one coronary artery. Eligible patients were randomly assigned (1:1) to either ticagrelor or placebo, by use of an interactive voice-response or web-response system. The THEMIS-PCI trial comprised a prespecified subgroup of patients with previous PCI. The primary efficacy outcome was a composite of cardiovascular death, myocardial infarction, or stroke (measured in the intention-to-treat population). Findings: Between Feb 17, 2014, and May 24, 2016, 11 154 patients (58% of the overall THEMIS trial) with a history of previous PCI were enrolled in the THEMIS-PCI trial. Median follow-up was 3·3 years (IQR 2·8–3·8). In the previous PCI group, fewer patients receiving ticagrelor had a primary efficacy outcome event than in the placebo group (404 [7·3%] of 5558 vs 480 [8·6%] of 5596; HR 0·85 [95% CI 0·74–0·97], p=0·013). The same effect was not observed in patients without PCI (p=0·76, p interaction=0·16). The proportion of patients with cardiovascular death was similar in both treatment groups (174 [3·1%] with ticagrelor vs 183 (3·3%) with placebo; HR 0·96 [95% CI 0·78–1·18], p=0·68), as well as all-cause death (282 [5·1%] vs 323 [5·8%]; 0·88 [0·75–1·03], p=0·11). TIMI major bleeding occurred in 111 (2·0%) of 5536 patients receiving ticagrelor and 62 (1·1%) of 5564 patients receiving placebo (HR 2·03 [95% CI 1·48–2·76], p<0·0001), and fatal bleeding in 6 (0·1%) of 5536 patients with ticagrelor and 6 (0·1%) of 5564 with placebo (1·13 [0·36–3·50], p=0·83). Intracranial haemorrhage occurred in 33 (0·6%) and 31 (0·6%) patients (1·21 [0·74–1·97], p=0·45). Ticagrelor improved net clinical benefit: 519/5558 (9·3%) versus 617/5596 (11·0%), HR=0·85, 95% CI 0·75–0·95, p=0·005, in contrast to patients without PCI where it did not, p interaction=0·012. Benefit was present irrespective of time from most recent PCI. Interpretation: In patients with diabetes, stable coronary artery disease, and previous PCI, ticagrelor added to aspirin reduced cardiovascular death, myocardial infarction, and stroke, although with increased major bleeding. In that large, easily identified population, ticagrelor provided a favourable net clinical benefit (more than in patients without history of PCI). This effect shows that long-term therapy with ticagrelor in addition to aspirin should be considered in patients with diabetes and a history of PCI who have tolerated antiplatelet therapy, have high ischaemic risk, and low bleeding risk