Publikation

Language-Independent Sentiment Analysis with Surrounding Context Extension

Outline:

T. Kincl, M. Novak, J. Pribil, P. Strach - Language-Independent Sentiment Analysis with Surrounding Context Extension - Social Computing and Social Media, Los Angeles, Vereinigte Staaten von Amerika, 2015, pp. 158-168

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.

Personen:

  • Dozent Ing. Pavel Strach Ph.D., Ph.D.

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