W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis

Authors: Aitor García Pablos Montserrat Cuadros Oller German Rigau Claramunt

Date: 31.01.2018

Expert Systems with Applications


Abstract

With the increase of online customer opinions in specialised websites and social networks, automatic systems to help organise and classify customer reviews by domain-specific aspect categories and sentiment polarity are more needed than ever. Supervised approaches for Aspect Based Sentiment Analysis achieve good results for the domain and language they are trained on, but manually labelling data to train supervised systems for all domains and languages is very costly and time consuming. In this work, we describe W2VLDA, an almost unsupervised system based on topic modelling that, combined with some other unsupervised methods and a minimal configuration step, performs aspect category classification, aspect-term and opinion-word separation and sentiment polarity classification for any given domain and language. We evaluate its domain aspect and sentiment classification performance in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several domains (hotels, restaurants, electronic devices) and languages (English, Spanish, French and Dutch).

BIB_text

@Article {
title = {W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis},
journal = {Expert Systems with Applications},
pages = {127-137},
volume = {91},
keywds = {
Almost unsupervised, Aspect Based Sentiment Analysis, Multidomain, Multilingual, Opinion mining
}
abstract = {

With the increase of online customer opinions in specialised websites and social networks, automatic systems to help organise and classify customer reviews by domain-specific aspect categories and sentiment polarity are more needed than ever. Supervised approaches for Aspect Based Sentiment Analysis achieve good results for the domain and language they are trained on, but manually labelling data to train supervised systems for all domains and languages is very costly and time consuming. In this work, we describe W2VLDA, an almost unsupervised system based on topic modelling that, combined with some other unsupervised methods and a minimal configuration step, performs aspect category classification, aspect-term and opinion-word separation and sentiment polarity classification for any given domain and language. We evaluate its domain aspect and sentiment classification performance in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several domains (hotels, restaurants, electronic devices) and languages (English, Spanish, French and Dutch).


}
doi = {10.1016/j.eswa.2017.08.049},
date = {2018-01-31},
}
Vicomtech

Parque Científico y Tecnológico de Gipuzkoa,
Paseo Mikeletegi 57,
20009 Donostia / San Sebastián (Spain)

+(34) 943 309 230

close overlay