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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Novel Framework for Online Sales Burst Prediction Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Stance Classification of Tweets Using Skip Char Ngrams

verfasst von : Yaakov HaCohen-kerner, Ziv Ido, Ronen Ya’akobov

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.

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 SINAS: Suspect Investigation Using Offenders’ Activity Space
Nächstes Kapitel Structural Semantic Models for Automatic Analysis of Urban Areas
Literatur
1.
2.
Mohammad, S.M., Zhu, X., Kiritchenko, S., Martin, J.: Sentiment, emotion, purpose, and style in electoral tweets. Inf. Process. Manage. 51(4), 480–499 (2015)CrossRef
3.
Basave, C., He, A.E., He, Y., Liu, K., Zhao, J.: A weakly supervised bayesian model for violence detection in social media (2013)
4.
Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116–124. Association for Computational Linguistics (2010)
5.
Murakami, A., Raymond, R.: Support or oppose? Classifying positions in online debates from reply activities and opinion expressions. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 869–875. Association for Computational Linguistics (2010)
6.
Anand, P., Walker, M., Abbott, R., Tree, J.E.F., Bowmani, R., Minor, M.: Cats rule and dogs drool!: classifying stance in online debate. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 1–9. Association for Computational Linguistics (2011)
7.
Sridhar, D., Foulds, J., Huang, B., Getoor, L., Walker, M.: Joint models of disagreement and stance in online debate. In: Annual Meeting of the Association for Computational Linguistics (2015)
8.
Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from Congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327–335. Association for Computational Linguistics (2006)
9.
Yessenalina, A., Yue, Y., Cardie, C.: Multi-level structured models for document-level sentiment classification. In: Proceedings of EMNLP, pp. 1046–1056 (2010)
10.
Burfoot, C., Bird, S., Baldwin, T.: Collective classification of congressional floor-debate transcripts. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 1506–1515. Association for Computational Linguistics (2011)
11.
Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceedings of WWW, pp. 529–535 (2003)
12.
Rajendran, P., Bollegala, D., Parsons, S.: Contextual stance classification of opinions: a step towards enthymeme reconstruction in online reviews. In: Proceedings of the 3rd Workshop on Argument Mining, pp. 31–39. Association for Computational Linguistics, Berlin (2016)
13.
Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A closer look at skip-gram modelling. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC-2006), pp. 1222–1225 (2006)
14.
Mohammad, S.M., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: SemEval-2016 task 6: detecting stance in tweets. In: Proceedings of SemEval, pp. 31–41 (2016)
15.
16.
Jans, B., Bethard, S., Vulić, I., Moens, M.F.: Skip n-grams and ranking functions for predicting script events. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 336–344. Association for Computational Linguistics (2012)
17.
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic n-grams as machine learning features for natural language processing. Expert Syst. Appl. 41(3), 853–860 (2014)CrossRef
18.
Fernández, J., Gutiérrez, Y., Gómez, J.M., Martınez-Barco, P.: GPLSI: supervised sentiment analysis in twitter using skipgrams. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), number SemEval, pp. 294–299 (2014)
19.
Dhondt, E., Verberne, S., Weber, N., Koster, C., Boves, L.: Using skipgrams and pos-based feature selection for patent classification. Comput. Linguist. Neth. J. 2, 52–70 (2012)
20.
HaCohen-Kerner, Y., Beck, H., Yehudai, E., Rosenstein, M., Mughaz, D.: Cuisine: classification using stylistic feature sets and/or name-based feature sets. J. Am. Soc. Inform. Sci. Technol. 61(8), 1644–1657 (2010)
21.
HaCohen-Kerner, Y., Beck, H., Yehudai, E., Mughaz, D.: Stylistic feature sets as classifiers of documents according to their historical period and ethnic origin. Appl. Artif. Intell. 24(9), 847–862 (2010)CrossRefMATH
22.
23.
24.
25.
26.
27.
Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 423–430. Association for Computational Linguistics (2003)‏
28.
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Mateo (2005)MATH
29.
Hall, M.: Correlation-based feature selection for machine learning. Doctoral dissertation, The University of Waikato (1999)‏
30.
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Icml, pp. 412–420 (1997)
31.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. ACM SIGKDD Explor. Newsl. 11(1), 10 (2009)CrossRef
32.
Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)
Metadaten
Titel
Stance Classification of Tweets Using Skip Char Ngrams
verfasst von
Yaakov HaCohen-kerner
Ziv Ido
Ronen Ya’akobov
Copyright-Jahr
2017
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71272-7

Electronic ISBN: 978-3-319-71273-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-71273-4_22