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2018 | OriginalPaper | Buchkapitel

Challenges in the Field of Aspect Level Sentiment Analysis

verfasst von : Neha Nandal, Jyoti Pruthi, Amit Choudhary

Erschienen in: Smart Trends in Information Technology and Computer Communications

Verlag: Springer Singapore

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Abstract

In the field of technology, organizations come up with their brandlines and it is becoming a trend where organizations wisely launch their on-series of their respective sources and then put it offline. The field of sentiment analysis has been playing a great role for organizations. It is becoming possible now to get to know about the opinions of customers about various sources produced by organizations in terms of positive, negative and neutral polarities. The field of aspect-level sentiment analysis comprises a goal to find and aggregate sentiment on entities mentioned within documents. This paper presents the various challenges occurred in field of sentiment analysis and Aspect level sentiment analysis. The objective is also to present the methods and tools used by various researchers to get the effective results in field of machine learning.

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Literatur
2.
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
3.
Zurück zum Zitat Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10, pp. 1320–1326 (2010) Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10, pp. 1320–1326 (2010)
4.
Zurück zum Zitat Tchalakova, M., Gerdemann, D., Meurers, D.: Automatic sentiment classification of product reviews using maximal phrases based analysis. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, ACL-HLT 2011, 24, June 2011, pp. 111–117 (2011) Tchalakova, M., Gerdemann, D., Meurers, D.: Automatic sentiment classification of product reviews using maximal phrases based analysis. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, ACL-HLT 2011, 24, June 2011, pp. 111–117 (2011)
5.
Zurück zum Zitat Yu, J., Zha, Z.-J., Wang, M., Chua, T.-S.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, Oregon, 19–24 June 2011, pp. 1496–1505 (2011) Yu, J., Zha, Z.-J., Wang, M., Chua, T.-S.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, Oregon, 19–24 June 2011, pp. 1496–1505 (2011)
6.
Zurück zum Zitat Bross, J., Ehrig, H.: Automatic construction of domain and aspect specific sentiment lexicons for customer review mining. In: CIKM 2013. ACM, 13/10, 27 October–1 November 2013. ISBN 978-1-4503-2263-8 Bross, J., Ehrig, H.: Automatic construction of domain and aspect specific sentiment lexicons for customer review mining. In: CIKM 2013. ACM, 13/10, 27 October–1 November 2013. ISBN 978-1-4503-2263-8
7.
Zurück zum Zitat Mesnil, G., Mikolov, T., Ranzato, M.A., Bengio, Y.: Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv organization, arXiv preprint arXiv:1412.5335 (2014) Mesnil, G., Mikolov, T., Ranzato, M.A., Bengio, Y.: Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv organization, arXiv preprint arXiv:​1412.​5335 (2014)
9.
Zurück zum Zitat Klenner, M., Tron, S., Amsler, M., Hollenatein, N.: The detection and analysis of bi-polar phrases and polarity conflicts. In: Proceedings of 11th International Workshop on Natural Language Processing and Cognitive Science, Venice, Italy (2014) Klenner, M., Tron, S., Amsler, M., Hollenatein, N.: The detection and analysis of bi-polar phrases and polarity conflicts. In: Proceedings of 11th International Workshop on Natural Language Processing and Cognitive Science, Venice, Italy (2014)
10.
Zurück zum Zitat Dong, L., Wei, F., Zhou, M., Xu, K.: Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014) Dong, L., Wei, F., Zhou, M., Xu, K.: Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
11.
Zurück zum Zitat Li, F., Wang, S., Liu, S., Zhang, M.: SUIT: a supervised user item based topic model for sentiment analysis. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014) Li, F., Wang, S., Liu, S., Zhang, M.: SUIT: a supervised user item based topic model for sentiment analysis. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
12.
Zurück zum Zitat West, R., Paskov, H.S., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. arXiv preprint arXiv:1409.2450 (2014) West, R., Paskov, H.S., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. arXiv preprint arXiv:​1409.​2450 (2014)
13.
Zurück zum Zitat Gryc, W., Moilanen, K.: Leveraging textual sentiment analysis with social network modelling. From Text Polit. Pos. Text Anal. Across Discip. 55, 47 (2014)CrossRef Gryc, W., Moilanen, K.: Leveraging textual sentiment analysis with social network modelling. From Text Polit. Pos. Text Anal. Across Discip. 55, 47 (2014)CrossRef
14.
Zurück zum Zitat Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)CrossRef Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)CrossRef
15.
Zurück zum Zitat Shefrin, H.: Investors’ judgments, asset pricing factors and sentiment. Eur. Fin. Manage. 21(2), 205–227 (2015)CrossRef Shefrin, H.: Investors’ judgments, asset pricing factors and sentiment. Eur. Fin. Manage. 21(2), 205–227 (2015)CrossRef
17.
Zurück zum Zitat Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2016)CrossRef Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2016)CrossRef
18.
Zurück zum Zitat Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic Tweets using deep convolutional neural networks. In: COLING 2016 arXiv:1610.08815[cs.CL] (2016) Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic Tweets using deep convolutional neural networks. In: COLING 2016 arXiv:​1610.​08815[cs.​CL] (2016)
19.
Zurück zum Zitat del Pilar Salas-Zárate, M., et. al: Sentiment analysis on Tweets about diabetes: an aspect-level approach. In: Computational and Mathematical Methods in Medicine, vol. 2017, Article ID 5140631 (2017) del Pilar Salas-Zárate, M., et. al: Sentiment analysis on Tweets about diabetes: an aspect-level approach. In: Computational and Mathematical Methods in Medicine, vol. 2017, Article ID 5140631 (2017)
20.
Zurück zum Zitat Virmani, D., Malhotra, V., Tyagi, R.: Sentiment analysis using collaborated opinion mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), July 2013 Virmani, D., Malhotra, V., Tyagi, R.: Sentiment analysis using collaborated opinion mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), July 2013
21.
Zurück zum Zitat Mohan, A., Manisha, R., Vijayaa, B., Naren, J.: An approach to perform aspect level sentiment analysis on customer reviews using sentiscore algorithm and priority based classification. (IJCSIT) Int. J. Comput. Sci. Inf. Technol. Mohan, A., Manisha, R., Vijayaa, B., Naren, J.: An approach to perform aspect level sentiment analysis on customer reviews using sentiscore algorithm and priority based classification. (IJCSIT) Int. J. Comput. Sci. Inf. Technol.
Metadaten
Titel
Challenges in the Field of Aspect Level Sentiment Analysis
verfasst von
Neha Nandal
Jyoti Pruthi
Amit Choudhary
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-1423-0_7

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