Language-Independent Sentiment Analysis with Surrounding Context Extension

Tomas Kincl, Michal Novak, Jiri Pribil, Pavel Strach

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationSocial Computing and Social Media - 7th International Conference, SCSM 2015 Held as Part of HCI International 2015, Proceedings
EditorsGabriele Meiselwitz
Number of pages11
ISBN (Print)978-3-319-20366-9
Publication statusPublished - 2015
Event7th International Conference on Social Computing and Social Media - Los Angeles, United States
Duration: 2 Aug 20157 Aug 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Social Computing and Social Media
Country/TerritoryUnited States
CityLos Angeles


  • Sentiment analysis
  • cross-domain
  • cross-language
  • surrounding context
  • Document surrounding context
  • Cross-language
  • Cross-domain


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