# Video Games as a Corpus: Sentiment Analysis using Fallout New Vegas Dialog

arXiv: https://arxiv.org/abs/2212.02168 · HTML: https://mlsanity.com/paper/2212.02168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis](https://mlsanity.com/paper/2106.15231/) (2021)
- [Structured Sentiment Analysis as Dependency Graph Parsing](https://mlsanity.com/paper/2105.14504/) (2021)
- [Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance](https://mlsanity.com/paper/2008.11573/) (2020)
- [When Word Embeddings Become Endangered](https://mlsanity.com/paper/2103.13275/) (2021)
- [Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP](https://mlsanity.com/paper/2207.04453/) (2022)

## Built on by (most cited first, 1 of 1)

- [Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages](https://mlsanity.com/paper/2305.15380/) (2023)

## Abstract (arXiv)

We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game in one of the 8 different sentiments: textitanger, disgust, fear, happy, neutral, pained, sad  and textitsurprised. The game has been translated into English, Spanish, German, French and Italian. We conduct experiments on multilingual, multilabel sentiment analysis on the extracted data set using multilingual BERT, XLMRoBERTa and language specific BERT models. In our experiments, multilingual BERT outperformed XLMRoBERTa for most of the languages, also language specific models were slightly better than multilingual BERT for most of the languages. The best overall accuracy was 54% and it was achieved by using multilingual BERT on Spanish data. The extracted data set presents a challenging task for sentiment analysis. We have released the data, including the testing and training splits, openly on Zenodo. The data set has been shuffled for copyright reasons.