A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining

TL;DR

This paper introduces a multilingual topic and sentiment analysis model based on deep neural networks, demonstrated on social media and news data about organic food, enabling cross-cultural comparison of themes.

Contribution

It presents a simple, effective method for joint multilingual topic modeling and sentiment analysis using pre-trained neural networks, with demonstrated application and reproducibility resources.

Findings

Themes match across languages in the domain studied.

High proportion of stable, domain-relevant topics obtained.

Model provides interpretable social media representations.

Abstract

User-generated content from social media is produced in many languages, making it technically challenging to compare the discussed themes from one domain across different cultures and regions. It is relevant for domains in a globalized world, such as market research, where people from two nations and markets might have different requirements for a product. We propose a simple, modern, and effective method for building a single topic model with sentiment analysis capable of covering multiple languages simultanteously, based on a pre-trained state-of-the-art deep neural network for natural language understanding. To demonstrate its feasibility, we apply the model to newspaper articles and user comments of a specific domain, i.e., organic food products and related consumption behavior. The themes match across languages. Additionally, we obtain an high proportion of stable and domain-relevant topics, a meaningful relation between topics and their respective textual contents, and an interpretable representation for social media documents. Marketing can potentially benefit from our method, since it provides an easy-to-use means of addressing specific customer interests from different market regions around the globe. For reproducibility, we provide the code, data, and results of our study.