Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Greedy Graph Edit Distance Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

SentiSAIL: Sentiment Analysis in English, German and Russian

verfasst von : Gayane Shalunts, Gerhard Backfried

Erschienen in: Machine Learning and Data Mining in Pattern Recognition

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Sentiment analysis has been well in the focus of researchers in recent years. Nevertheless despite the considerable amount of literature in the field the majority of publications target the domains of movie and product reviews in English. The current paper presents a novel sentiment analysis method, which extends the state-of-the-art by trilingual sentiment classification in the domains of general news and particularly the coverage of natural disasters in general news. The languages examined are English, German and Russian. The approach targets both traditional and social media content. The extensive experiments demonstrate that the performance of the proposed approach outperforms human annotators, as well as the original method, on which it is built and extended.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel IKLTSA: An Incremental Kernel LTSA Method
Nächstes Kapitel Sentiment Analysis for Government: An Optimized Approach
Fußnoten
Literatur
1.
Backfried, G., Schmidt, C., Pfeiffer, M., Quirchmayr, G., Glanzer, M., Rainer, K.: Open source intelligence in disaster management. In: Proceedings of the European Intelligence and Security Informatics Conference (EISIC), pp. 254–258. IEEE Computer Society, Odense, Denmark (2012)
2.
Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., van der Goot, E., Halkia, M., Pouliquen, B., Belyaeva, J.: Sentiment analysis in the news. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010). European Language Resources Association (ELRA), Valletta, Malta (2010)
3.
Brooke, J., Tofiloski, M., Taboada, M.: Cross-linguistic sentiment analysis: from english to spanish. In: Proceedings of Recent Advances in Natural Language Processing (RANLP), pp. 50–54. RANLP 2009 Organising Committee / ACL, Borovets, Bulgaria (2009)
4.
Cambria, E., Speer, R., Havasi, C., Hussain, A.: Senticnet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, pp. 14–18 (2010)
5.
Chetviorkin, I., Loukachevitch, N.: Extraction of russian sentiment lexicon for product meta-domain. In: Proceedings of the 24th International Conference on Computational Linguistics (COLING), Bombay, India, pp. 593–610 (2012)
6.
Chetviorkin, I., Loukachevitch, N.: Evaluating sentiment analysis systems in russian. In: Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing, Sofia, Bulgaria, pp. 12–17 (2013)
7.
Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2009)CrossRef
8.
Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06), pp. 417–422 (2006)
9.
Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the 1st ACM Conference on Online Social Networks (COSN 2013), pp. 27–38. ACM, Boston, USA (2013)
10.
Gonçalves, P., Benevenuto, F., Cha, M.: Panas-t: A pychometric scale for measuring sentiments on twitter. CoRR abs/1308.1857 (2013)
11.
Missen, M.M.S., Boughanem, M., Cabanac, G.: Opinion mining: reviewed from word to document level. Soc. Netw. Anal. Min. 3(1), 107–125 (2013)CrossRef
12.
Momtazi, S.: Fine-grained german sentiment analysis on social media. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012), pp. 1215–1220. European Language Resources Association (ELRA), Istanbul, Turkey (2012)
13.
Norman, G.J., Norris, C.J., Gollan, J., Ito, T.A., Hawkley, L.C., Larsen, J.T., Cacioppo, J.T., Berntson, G.G.: Current emotion research in psychophysiology: the neurobiology of evaluative bivalence. Emot. Rev. 3(3), 349–359 (2011)CrossRef
14.
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, PA, USA, pp. 79–86 (2002)
15.
Remus, R., Quasthoff, U., Heyer, G.: Sentiws - a german-language resource for sentiment analysis. In: Proceedings of the 7th conference on International Language Resources and Evaluation (LREC), Valletta, Malta, pp. 1168–1171 (2010)
16.
Shalunts, G., Backfried, G., Prinz, K.: Sentiment analysis of German social media data for natural disasters. In: Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pp. 752–756. University Park, Pennsylvania, USA (2014)
17.
Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)
18.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRef
19.
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: liwc and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 25–54 (2010)CrossRef
20.
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRef
21.
Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: ACL (System Demonstrations), pp. 115–120 (2012)
22.
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)CrossRef
Metadaten
Titel
SentiSAIL: Sentiment Analysis in English, German and Russian
verfasst von
Gayane Shalunts
Gerhard Backfried
Copyright-Jahr
2015
Buch
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_6

Premium Partner

    Bildnachweise