# W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis

## TL;DR

W2VLDA is an almost unsupervised, multilingual system for aspect-based sentiment analysis that leverages topic modeling and minimal configuration to classify aspects and sentiments across domains and languages.

## Contribution

It introduces a novel almost unsupervised approach combining topic modeling with other methods for domain-specific aspect and sentiment classification.

## Key findings

- Achieves competitive results on multilingual SemEval 2016 dataset
- Effective across multiple languages and domains
- Requires minimal supervision and configuration

## Abstract

With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever. Supervised approaches to Aspect Based Sentiment Analysis obtain good results for the domain/language their are trained on, but having manually labelled data for training supervised systems for all domains and languages are usually 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, performs aspect/category classifiation, aspect-terms/opinion-words separation and sentiment polarity classification for any given domain and language. We evaluate the performance of the aspect and sentiment classification in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several languages (English, Spanish, French and Dutch) and domains (hotels, restaurants, electronic-devices).