A Case Study of Machine Translation in Financial Sentiment Analysis

The European research project Social Sentiment Indices powered by X-Scores (SSIX) intends to allow Small and Medium-sized Enterprises (SMEs) to take advantage of social media sentiment data for the finance domain. The project aims to overcome language barriers and realize a financial sentiment platform capable of scoring textual data in different languages.

Our approach to achieve this goal takes maximum advantage of human translation while keeping costs low by incorporating machine translation. In the long run, we intend to provide a tool that helps SMEs to expand into new markets by analyzing multilingual social contents.

In this paper, we investigate how sentiment is preserved after machine translation. We built a sentiment gold standard corpus in English annotated by native financial experts, and then we translated the gold standard corpus into a target corpus (German) using one human translator and three machine translation engines (Microsoft, Google, and Google Neural Network) which are integrated in Geofluent to allow pre-/post-processing. We then conducted two experiments. One meant to evaluate the overall translation quality using the BLEU algorithm. The other intended to investigate which machine translation engines produce translations that preserve sentiment best.

Results suggest that sentiment transfer can be successful through machine translation if using Google and Google Neural Network in Geofluent. This is a crucial step towards achieving a multilingual sentiment platform in the domain of finance. Next, we plan to integrate language-specific processing rules to further enhance the performance of machine translation.