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6
para Mineŕıa de Opiniones Basada en Aspectos
"... Unsupervised acquisition of domain aspect terms ..."
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V3: Unsupervised Generation of Domain Aspect Terms for Aspect Based Sentiment Analysis
"... This paper presents V3, an unsupervised system for aspect-based Sentiment Analy-sis when evaluated on the SemEval 2014 Task 4. V3 focuses on generating a list of aspect terms for a new domain using a collection of raw texts from the domain. We also implement a very basic approach to classify the asp ..."
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This paper presents V3, an unsupervised system for aspect-based Sentiment Analy-sis when evaluated on the SemEval 2014 Task 4. V3 focuses on generating a list of aspect terms for a new domain using a collection of raw texts from the domain. We also implement a very basic approach to classify the aspect terms into categories and assign polarities to them. 1
TriRank: Review-aware Explainable Recommendation by Modeling Aspects
, 2015
"... Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by ex-tracting from user ratings. Aside from users ’ ratings, their affiliated reviews often provide the rationale for their rat-ings and identify what aspects of the item they cared most ..."
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Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by ex-tracting from user ratings. Aside from users ’ ratings, their affiliated reviews often provide the rationale for their rat-ings and identify what aspects of the item they cared most about. We explore the rich evidence source of aspects in user reviews to improve top-N recommendation. By extracting aspects (i.e., the specific properties of items) from textual reviews, we enrich the user–item binary relation to a user– item–aspect ternary relation. We model the ternary relation as a heterogeneous tripartite graph, casting the recommen-dation task as one of vertex ranking. We devise a generic algorithm for ranking on tripartite graphs — TriRank — and specialize it for personalized recommendation. Experiments on two public review datasets show that it consistently out-performs state-of-the-art methods. Most importantly, Tri-Rank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews. It allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.
IHS R&D Belarus: Cross-domain Extraction of Product Features using Conditional Random Fields
"... This paper describes the aspect extraction system submitted by IHS R&D Belarus team at the SemEval-2014 shared task re-lated to Aspect-Based Sentiment Analy-sis. Our system is based on IHS Goldfire linguistic processor and uses a rich set of lexical, syntactic and statistical features in CRF mod ..."
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This paper describes the aspect extraction system submitted by IHS R&D Belarus team at the SemEval-2014 shared task re-lated to Aspect-Based Sentiment Analy-sis. Our system is based on IHS Goldfire linguistic processor and uses a rich set of lexical, syntactic and statistical features in CRF model. We participated in two domain-specific tasks – restaurants and laptops – with the same system trained on a mixed corpus of reviews. Among sub-missions of constrained systems from 28 teams, our submission was ranked first in laptop domain and fourth in restaurant domain for the subtask A devoted to as-pect extraction. 1
RESEARCH ARTICLE Resource Construction and Evaluation for Indirect Opinion Mining of Drug Reviews
"... Opinion mining is a well-known problem in natural language processing that has attracted increasing attention in recent years. Existing approaches are mainly limited to the identifica-tion of direct opinions and are mostly dedicated to explicit opinions. However, in some do-mains such as medical, th ..."
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Opinion mining is a well-known problem in natural language processing that has attracted increasing attention in recent years. Existing approaches are mainly limited to the identifica-tion of direct opinions and are mostly dedicated to explicit opinions. However, in some do-mains such as medical, the opinions about an entity are not usually expressed by opinion words directly, but they are expressed indirectly by describing the effect of that entity on other ones. Therefore, ignoring indirect opinions can lead to the loss of valuable information and noticeable decline in overall accuracy of opinion mining systems. In this paper, we first introduce the task of indirect opinion mining. Then, we present a novel approach to con-struct a knowledge base of indirect opinions, called OpinionKB, which aims to be a resource for automatically classifying people’s opinions about drugs. Using our approach, we have extracted 896 quadruples of indirect opinions at a precision of 88.08 percent. Furthermore, experiments on drug reviews demonstrate that our approach can achieve 85.25 percent precision in polarity detection task, and outperforms the state-of-the-art opinion mining methods. We also build a corpus of indirect opinions about drugs, which can be used as a basis for supervised indirect opinion mining. The proposed approach for corpus construc-tion achieves the precision of 88.42 percent.
MEASURING THE INFLUENCE OF MAINSTREAM MEDIA
, 2014
"... Measuring the influence of mainstream media on twitter users ..."
