nach oben

Cluster Computing

Erschienen in:

24.06.2017

Sentiment analysis of short texts in microblog based on ependency parsing

verfasst von: Lirong Qiu, Jie Li

Erschienen in: Cluster Computing | Ausgabe 1/2018

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Traditional approaches to analyzing short text sentiment rarely consider the relationship between emotional words and modifiers. Most traditional methods simply accumulate the sentiment of the sentence to obtain the sentiment of short text. In this paper, we propose a method to mitigate the problems through sentiment structure and sentiment calculation rules. The sentiment structure is obtained from the dependency parsing process with the relationship migration and modified distance, which makes a solid contribution to analyzing the sentiment of short text. The sentiment of short text is accumulated according to the different influence of relationships between the clauses and the contribution of each sentence to the sentiment calculation of short text. Experiment result indicates the effective of the approach.

Vorheriger Artikel The artificial stock market model based on agent and scale-free network

Nächster Artikel Large deviations for the stochastic present value of aggregate net claims with infinite variance in the renewal risk model and its application in risk management

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

Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 168–177 (2004)

Zhao, Y.Y., Qin, B., Liu, T.: Sentiment analysis. J. Softw. 21(8), 1834–1848 (2010)CrossRef

Yang, A., Lin, J., Zhou, Y.: Method on building Chinese text sentiment Lexicon. J. Front. Comput. Sci. Technol. 7(11), 1033–1039 (2013)

Xu, G., Meng, X., Wang, H.: Build Chinese emotion lexicons using a graph-based algorithm and multiple resources. In: International conference on computational linguistics, proceedings of the conference, 23–27 August 2010, Beijing, China, pp. 1209–1217 (2010)

Jiang, D., Luo, X., Xuan, J., Xu, Z.: Sentiment computing for the news event based on the social media big data. IEEE Access 5, 2373–2382 (2017)CrossRef

Wang, X., Zhang, H., Xu, Z.: Public Sentiments Analysis Based on Fuzzy Logic for Text. International Journal of Software Engineering and Knowledge Engineering 26(9–10), 1341–1360 (2016)CrossRef

Taboada, M., Brooke, J., Tofiloski, M., et al.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRef

Xia, R., Wang, C., Dai, X.Y., et al.: Co-training for Semi-supervised sentiment classification based on dual-view bags-of-words representation. ACL (1), 1054–1063 (2015)

Tang, D., Wei, F., Qin, B., et al.: Building large-scale Twitter-specific sentiment Lexicon: a representation learning approach. COLING, 172–182 (2014)

10.

Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, Vol. 10. Association for Computational Linguistics, pp. 79–86 (2002)

11.

Cui, H., Mittal, V., Datar, M.: Comparative experiments on sentiment classification for online product reviews. AAAI 6, 1265–1270 (2006)

12.

Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of COLING 2010, Beijing, China, pp. 36–44 (2010)

13.

Zhao, J., Liu, K., Wang, G.: Adding redundant features for CRFs-based sentence sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp. 117–126 (2008)

14.

Agarwal, A., Xie, B., Vovsha, I., et al.: Sentiment analysis of Twitter data. In: Proceedings of the workshop on language in social media (LSM 2011), Portland, Oregon, pp. 30–38 (2011)

15.

Zhang, C.G., Liu, P.Y., Zhu, Z.F., et al.: A sentiment analysis method based on a polarity lexicon. J. Shandong Univ. 47(3), 47–50 (2012)

16.

Shi, F., Fu, Y., Feng, Y., et al.: Blog sentiment orientation analysis based on dependency parsing. J. Comput. Res. Dev. 49(11), 2395–2406 (2012)

17.

Zhang, S., Yu, L., Hu, C.: Sentiment analysis of Chinese micro-blog based on emotions and emotional words. Comput. Sci. 39(11A), 146–176 (2012)

18.

Xie, L., Zhou, M., Sun, M.: Hierarchical structure based hybrid approach to sentiment analysis of Chinese micro blog and its feature extraction. J. Chin. Inf. Process. 26(1), 73–83 (2012)

19.

Yao, T.F., Nie, Q.Y., Li, J.C., et al.: An opinion mining system for Chinese automobile reviews. Front. Chin. Inf. Process., 260–281 (2006)

20.

Yang, L., Geng, X., Liao, H.: A web sentiment analysis method on fuzzy clustering for mobile social media users. Eurasip J. Wirel. Commun. Netw. 2016(1), 1–13 (2016)CrossRef

21.

Li, A., Di, P., Duan, L.: Document sentiment orientation analysis based on sentence weighted algorithm. J. Chin. Comput. Syst. 36(10), 2252–2256 (2015)

22.

Li, S., Zhang, H., Xu, W., et al. Chinese text sentiment analysis based on combination model. In: China Conference on Information Retrieval (2009)

23.

Liang, J., Chai, Y., Yuan, H., et al.: Deep learning for Chinese micro-blog sentiment analysis. J. Chin. Inf. Process. 28(5), 155–161 (2014)

24.

Sun, J., Xueqiang, L., Zhang, L.: On sentiment analysis of Chinese microblogging based on lexicon and machine learning. Comput. Appl. Softw. 7, 177–181 (2014)

25.

Cheng, N.C., Hou, M., Teng, Y.L.: Short text attitude analysis based on textual characteristics. J. Chin. Inf. Process. 29(3), 163–169 (2015)

26.

Li, A., Di, P., Duan, L.: Document sentiment orientation analysis based on sentence weighted algorithm. J. Chin. Comput. Syst. 36(10), 2252–2256 (2015)

27.

Kai, G.A.O., Siyu, L.I., Dongru, R.U.A.N., et al.: A micro-blog sentiment analysis approach. J. Chin. Inf. Process. 29(4), 40–49 (2015)

28.

Zhang, J., Sun, Y., Wang, H., et al.: Calculating statistical similarity between sentences. J. Converg. Inf. Technol, 6(2) (2011)

Titel: Sentiment analysis of short texts in microblog based on ependency parsing
verfasst von: Lirong Qiu
Jie Li
Publikationsdatum: 24.06.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 1/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-0995-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2018

Prediction method of icing thickness of transmission line based on MEAO

Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution

A loss prevention methodology for catering industry based on operation data analysis

DeepSIC: a deep model

Heart rate variability based stress index service model using bio-sensor

An IoT healthcare service model of a vehicle using implantable devices

Premium Partner