Skip to main content

2015 | OriginalPaper | Buchkapitel

AMRITA-CEN@SAIL2015: Sentiment Analysis in Indian Languages

verfasst von : Shriya Se, R. Vinayakumar, M. Anand Kumar, K. P. Soman

Erschienen in: Mining Intelligence and Knowledge Exploration

Verlag: Springer International Publishing

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

search-config
loading …

Abstract

The contemporary work is done as slice of the shared task in Sentiment Analysis in Indian Languages (SAIL 2015), constrained variety. Social media allows people to create and share or exchange opinions based on many perspectives such as product reviews, movie reviews and also share their thoughts through personal blogs and many more platforms. The data available in the internet is huge and is also increasing exponentially. Due to social media, the momentousness of categorizing these data has also increased and it is very difficult to categorize such huge data manually. Hence, an improvised machine learning algorithm is necessary for wrenching out the information. This paper deals with finding the sentiment of the tweets for Indian languages. These sentiments are classified using various features which are extracted using words and binary features, etc. In this paper, a supervised algorithm is used for classifying the tweets into positive, negative and neutral labels using Naive Bayes classifier.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Fink, C.R., et al.: Coarse- and fine-grained sentiment analysis of social media text. Johns Hopkins APL Tech. Dig. 30(1), 22–30 (2011)MathSciNet Fink, C.R., et al.: Coarse- and fine-grained sentiment analysis of social media text. Johns Hopkins APL Tech. Dig. 30(1), 22–30 (2011)MathSciNet
2.
Zurück zum Zitat Balahur, A.: Sentiment analysis in social media texts. In: 4th Workshop on Computational Approaches (2013) Balahur, A.: Sentiment analysis in social media texts. In: 4th Workshop on Computational Approaches (2013)
3.
Zurück zum Zitat Hutto, C.J., Gilbertl, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014) Hutto, C.J., Gilbertl, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)
4.
Zurück zum Zitat Arunselvan, S.J., Anand kumar, M., et al.: Sentiment analysis of tamil moovie reviews via feature frequency count. IJAER 10, 17934–17939 (2015) Arunselvan, S.J., Anand kumar, M., et al.: Sentiment analysis of tamil moovie reviews via feature frequency count. IJAER 10, 17934–17939 (2015)
5.
Zurück zum Zitat Jansen, B.J., et al.: Twitter power: tweets as electronic word of mouth. J. Am. Soc. Inf. Sci. Technol. 60(11), 2169–2188 (2009)CrossRef Jansen, B.J., et al.: Twitter power: tweets as electronic word of mouth. J. Am. Soc. Inf. Sci. Technol. 60(11), 2169–2188 (2009)CrossRef
6.
Zurück zum Zitat Hiroshi, K., et al.: Deeper sentiment analysis using machine translation technology. In: 20th International Conference on Computational Linguistics (2004) Hiroshi, K., et al.: Deeper sentiment analysis using machine translation technology. In: 20th International Conference on Computational Linguistics (2004)
7.
Zurück zum Zitat John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence (1995) John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence (1995)
8.
Zurück zum Zitat Godbole, N., et al.: Large-scale sentiment analysis for news and blogs. ICWSM 7, 21 (2007) Godbole, N., et al.: Large-scale sentiment analysis for news and blogs. ICWSM 7, 21 (2007)
9.
Zurück zum Zitat Kouloumpis, E.: Twitter sentiment analysis: the good the bad and the omg!. Icwsm 11, 538–541 (2011) Kouloumpis, E.: Twitter sentiment analysis: the good the bad and the omg!. Icwsm 11, 538–541 (2011)
10.
Zurück zum Zitat Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef
11.
12.
Zurück zum Zitat Turney, P.D., et al.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37(1), 141–188 (2010)MATHMathSciNet Turney, P.D., et al.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37(1), 141–188 (2010)MATHMathSciNet
13.
Zurück zum Zitat Rennie, J.D., et al.: Tackling the poor assumptions of naive bayes text classifiers. In: ICML, vol. 3 (2003) Rennie, J.D., et al.: Tackling the poor assumptions of naive bayes text classifiers. In: ICML, vol. 3 (2003)
14.
Zurück zum Zitat Jordan, A.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. Adv. Neural Inf. Process. Syst. 14, 841 (2002) Jordan, A.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. Adv. Neural Inf. Process. Syst. 14, 841 (2002)
15.
Zurück zum Zitat Panda, M., Abraham, A., Patra, M.R.: Discriminative multinomial naive bayes for network intrusion detection, pp. 5–10 (2010) Panda, M., Abraham, A., Patra, M.R.: Discriminative multinomial naive bayes for network intrusion detection, pp. 5–10 (2010)
16.
Zurück zum Zitat Juan, A., Ney, H.: Reversing and smoothing the multinomial naive bayes text classifier. In: PRIS (2002) Juan, A., Ney, H.: Reversing and smoothing the multinomial naive bayes text classifier. In: PRIS (2002)
17.
Zurück zum Zitat McCallum, A., Nigam, K.A.: Comparison of Event Models for Naive Bayes Text Classification. In: AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41–48 (1998) McCallum, A., Nigam, K.A.: Comparison of Event Models for Naive Bayes Text Classification. In: AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41–48 (1998)
18.
Zurück zum Zitat Lewis, D.D.: Naive bayes at forty: the independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398. Springer, Heidelberg (1998) Lewis, D.D.: Naive bayes at forty: the independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398. Springer, Heidelberg (1998)
19.
Zurück zum Zitat Amor, N.B., et al.: Naive bayes vs decision trees in intrusion detection systems. In: 2004 ACM Symposium on Applied Computing (2004) Amor, N.B., et al.: Naive bayes vs decision trees in intrusion detection systems. In: 2004 ACM Symposium on Applied Computing (2004)
Metadaten
Titel
AMRITA-CEN@SAIL2015: Sentiment Analysis in Indian Languages
verfasst von
Shriya Se
R. Vinayakumar
M. Anand Kumar
K. P. Soman
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-26832-3_67

Premium Partner