# W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis

arXiv: https://arxiv.org/abs/1705.07687 · HTML: https://mlsanity.com/paper/1705.07687/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 12 of 12)

- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.03806/) (2021)
- [A Comprehensive Overview of Recommender System and Sentiment Analysis](https://mlsanity.com/paper/2109.08794/) (2021)
- [Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction](https://mlsanity.com/paper/2112.09215/) (2021)
- [Fine-Grained Opinion Summarization with Minimal Supervision](https://mlsanity.com/paper/2110.08845/) (2021)
- [A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks](https://mlsanity.com/paper/2207.02160/) (2022)
- [Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels](https://mlsanity.com/paper/2310.06940/) (2023)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)
- [Exploring the Distribution Regularities of User Attention and Sentiment toward Product Aspects in Online Reviews](https://mlsanity.com/paper/2209.03690/) (2022)
- [From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision](https://mlsanity.com/paper/2305.01710/) (2023)
- [AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework](https://mlsanity.com/paper/2406.13947/) (2024)
- [Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2604.26619/) (2026)

## Abstract (arXiv)

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).