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2019 | OriginalPaper | Chapter

A Sentiment Analysis Lexical Resource and Dataset for Government Smart Apps Domain

Authors : Omar Alqaryouti, Nur Siyam, Khaled Shaalan

Published in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018

Publisher: Springer International Publishing

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Abstract

Sentiment resources are essential components for developing applications for Sentiment Analysis (SA). Common publicly available datasets such as products, restaurants and movies reviews usually fulfil the researchers needs in order to conduct their experiments. However, for specific domains, the needed dataset sources could be difficult to find. This signifies the need to construct domain specific datasets and lexicons which are vital to evaluate SA tasks. In this paper, we present the work that has been done in order to produce a unique dataset that consists of government smart apps domain aspects and opinion words. Additionally, we explain the approach that was carried out to measure the sentiment scores to opinion words and build the desired lexicons. A general-purpose data annotation and preparation tool was developed for facilitating the development of SA lexical resources and dataset for government smart apps domain.

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Literature
1.
Hasbullah, S.S., Maynard, D., Chik, R.Z.W., Mohd, F., Noor, M.: Automated content analysis. In: Proceedings of 10th International Conference on Ubiquitous Information Management and Communication, IMCOM 2016, pp. 1–6 (2016)
2.
Tan, S., Wu, Q.: A random walk algorithm for automatic construction of domain-oriented sentiment lexicon. Expert Syst. Appl. 38(10), 12094–12100 (2011)CrossRef
3.
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining SentiWordNet. Analysis 0, 1–12 (2010)
4.
Dang, Y., Zhang, Y., Chen, H.: A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell. Syst. 25(4), 46–53 (2010)CrossRef
5.
Steinberger, J., et al.: Creating sentiment dictionaries via triangulation. Decis. Support Syst. 53(4), 689–694 (2012)CrossRef
6.
Boldrini, E., Balahur, A., Martínez-Barco, P., Montoyo, A.: Using EmotiBlog to annotate and analyse subjectivity in the new textual genres. Data Min. Knowl. Discov. 25(3), 603–634 (2012)CrossRef
7.
Robaldo, L., Di Caro, L.: OpinionMining-ML. Comput. Stand. Interfaces 35(5), 454–469 (2013)CrossRef
8.
Sarmento, L., Carvalho, P., Silva, M.J., de Oliveira, E.: Automatic creation of a reference corpus for political opinion mining in user-generated content. In: Proceeding 1st International CIKM Workshop on Topic Analysis for Mass Opinion, TSA 2009, p. 29 (2009)
9.
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)CrossRef
10.
11.
12.
13.
14.
15.
Mills, A.: Certified Innovation Strategist, 1st edn. Global Innovation Institute, Boston (2016)
16.
Takala, P., Malo, P., Sinha, A., Ahlgren, O.: Gold-standard for topic-specific sentiment analysis of economic texts. In: LREC, pp. 2152–2157 (2014)
17.
Joshi, A., Bhattacharyya, P., Ahire, S.: Sentiment resources: lexicons and datasets. In: A Practical Guide to Sentiment Analysis, vol. 5, pp. 85–106. Springer, Berlin (2017)
18.
Poria, S., Cambria, E., Ku, L.-W., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: Second Workshop on Natural Language Processing for Social Media, pp. 28–37 (2014)
19.
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl. Based Syst. 108, 42–49 (2016)CrossRef
20.
Liu, Q., Gao, Z., Liu, B., Zhang, Y.: A logic programming approach to aspect extraction in opinion mining, pp. 276–283 (2013)
21.
Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W.: Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl. Based Syst. 37, 186–195 (2013)CrossRef
22.
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of 50th Annual Meeting Association Computational Linguistics Long Paper, ACL 2012, vol. 1, no. July, pp. 339–348 (2012)
23.
Montoyo, A., Martínez-Barco, P., Balahur, A.: Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis. Support Syst. 53(4), 675–679 (2012)CrossRef
Metadata
Title
A Sentiment Analysis Lexical Resource and Dataset for Government Smart Apps Domain
Authors
Omar Alqaryouti
Nur Siyam
Khaled Shaalan
Copyright Year
2019
Book
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018

Print ISBN: 978-3-319-99009-5

Electronic ISBN: 978-3-319-99010-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-99010-1_21

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