Language-independent sentiment analysis with surrounding context extension

Abstract

Expressing attitudes and opinions towards various entities (i.e. products, companies, people and events) has become pervasive with the recent proliferation of social media. Monitoring of what customers think is a key task for marketing research and opinion surveys, while measuring customers’ preferences or media monitoring have become a fundamental part of corporate activities. Most experiments on automated sentiment analysis focus on major languages (English, but also Chinese); minor or morphologically rich languages are addressed rather sparsely. Moreover, to improve the performance of machine-learning based classifiers, the models are often complemented with language-dependent components (i.e. sentiment lexicons). Such combined approaches provide a high level of accuracy but are limited to a single language or a single thematic domain. This paper aims to contribute to this field and introduces an experiment utilizing a language– and domain– independent model for sentiment analysis. The model has been previously tested on multiple corpora, providing a trade-off between generality and the classification performance of the model. In this paper, we suggest a further extension of the model utilizing the surrounding context of the classified documents.