Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data

TL;DR

This paper proposes a multilingual sentiment analysis framework using an English-trained RNN model and translation, demonstrating significant performance improvements across multiple languages with limited data.

Contribution

It introduces a novel approach of reusing an English-trained RNN sentiment model for other languages via translation, reducing resource requirements and model training efforts.

Findings

The English-trained model outperforms baseline models in multiple languages.

Translation-based reuse achieves statistically significant improvements.

The approach simplifies multilingual sentiment analysis with limited data.

Abstract

Sentiment analysis is a widely studied NLP task where the goal is to determine opinions, emotions, and evaluations of users towards a product, an entity or a service that they are reviewing. One of the biggest challenges for sentiment analysis is that it is highly language dependent. Word embeddings, sentiment lexicons, and even annotated data are language specific. Further, optimizing models for each language is very time consuming and labor intensive especially for recurrent neural network models. From a resource perspective, it is very challenging to collect data for different languages. In this paper, we look for an answer to the following research question: can a sentiment analysis model trained on a language be reused for sentiment analysis in other languages, Russian, Spanish, Turkish, and Dutch, where the data is more limited? Our goal is to build a single model in the language with the largest dataset available for the task, and reuse it for languages that have limited resources. For this purpose, we train a sentiment analysis model using recurrent neural networks with reviews in English. We then translate reviews in other languages and reuse this model to evaluate the sentiments. Experimental results show that our robust approach of single model trained on English reviews statistically significantly outperforms the baselines in several different languages.