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Information Systems Frontiers
Erschienen in: Information Systems Frontiers 3/2021

05.01.2021

An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis

verfasst von: A. Geethapriya, S. Valli

Erschienen in: Information Systems Frontiers | Ausgabe 3/2021

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Abstract

Domain adaptation in sentiment analysis is one of the areas where a classifier trained in one domain often classifies sentiments poorly when applied to another domain due to domain-specific words. Extracting features and their relevant opinion words from different domain sources and mapping them to the target domains are herculean tasks as far as domain adaptation is concerned. In this paper, the feature extraction technique is refined by which the mapping task is enhanced. The feature extraction technique uses both the syntactic and semantic properties of the features for extracting similar words. The features are further refined by merging synonyms and by replacing negative polarity terms with the appropriate antonyms. This refinement in the feature selection improves the mapping functionality of the domain adaptation and also exploits the relationship between domain-specific words and domain-independent words from different domains.
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Literatur
Abbasi, A., France, S., Zhang, Z., & Chen, H. (2011). Selecting attributes for sentiment classification using feature relation networks. IEEE Transactions on Knowledge and Data Engineering, 23(3), 447–462.CrossRef
Ando, R. K., & Zhang, T. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6). 1817–1853
Balamurali, A. R., Manna, D., & Bhattacharyya, P. (2010).Cross-domain sentiment tagging using meta-classifier and a high accuracy in-domain classifier. In proceedings of the ICON 2010: 8th international conference on natural language processing, 2010.
Bhattacharyya, P., (2015). Multilingual projections. In language production, cognition, and the lexicon (pp. 175–200). Springer, Cham.
Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. In proceedings of the 2006 conference on empirical methods in natural language processing (pp.120-128). Association for Computational Linguistics.
Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In proceedings of 45th annual meeting of the Association for Computational Linguistics, pp. 440-447.
Bollegala, D., Weir, D., & Carroll, J. (2013). Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1719–1731.CrossRef
Bollegala, D., Maehara, T., & Kawarabayashi, K. I. (2015). Unsupervised cross-domain word representation learning. arXiv preprint arXiv:1505.07184.
Cabitza, F., Locoro, A., & Batini, C. (2020). Making open data more personal through a social value perspective: A methodological approach. Information Systems Frontiers, 22(1), 131–148.CrossRef
Chang, W. L., & Chen, Y. P. (2019). Way too sentimental? A credible model for online reviews. Information Systems Frontiers, 21(2), 453–468.CrossRef
Chen, Z. (2020). A label proportions estimation technique for adversarial domain adaptation in text classification. arXiv preprint arXiv:2003.07444.
Choi, Y., & Lee, H. (2017). Data properties and the performance of sentiment classification for electronic commerce applications. Information Systems Frontiers, 19(5), 993–1012.CrossRef
Courty, N., Flamary, R., Tuia, D., & Rakotomamonjy, A. (2017). Optimal transport for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1853–1865.CrossRef
Cui, X., & Bollegala, D. (2020). Multi-source attention for unsupervised domain adaptation. arXiv preprint arXiv:2004.06608.
Daume III, H., & Marcu, D. (2006). Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26, 101–126.CrossRef
Deshmukh, Jyoti S., & Amiya Kumar Tripathy. (2017). Text classification using semi-supervised approach for multi domain. In 2017 International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1–5.
Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. In proceedings of the 2008 international conference on web search and data mining (pp. 231-240). ACM.
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R. and Lin, C.J. (2008). LIBLINEAR: A library for large linear classification. Journal of machine learning research, no.9, pp.1871–1874.
Ghag, K., & Shah, K. (2013).Comparative analysis of the techniques for sentiment analysis. International conference on advances in technology and engineering (ICATE), 2013, (pp. 1–7).
Guo, H., Zhu, H., Guo, Z., Zhang, X., Wu, X., & Su, Z. (2009). Domain adaptation with latent semantic association for named entity recognition. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 281–289).
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 168–177).ACM.
Hung, B. T. (2020). Domain-specific versus general-purpose word representations in sentiment analysis for deep learning models. In Frontiers in intelligent computing: Theory and applications (pp. 252–264). Springer, Singapore.
Ibeke, E., Lin, C., Wyner, A., & Barawi, M. H. (2020). A unified latent variable model for contrastive opinion mining. Frontiers of Computer Science, 14(2), 404–416.CrossRef
Jeong, H., Shin, D., & Choi, J. (2011). Ferom: Feature extraction and refinement for opinion mining. ETRI Journal, 33(5), 720–730.CrossRef
Kanayama, H., & Nasukawa, T. (2006).Fully automatic lexicon expansion for domain-oriented sentiment analysis. In proceedings of the 2006 conference on empirical methods in natural language processing (pp. 355-363).Association for Computational Linguistics.
Kayal, P., Singh, M., & Goyal, P. (2020). Weakly-supervised deep learning for domain invariant sentiment classification. In proceedings of the 7th ACM IKDD CoDS and 25th COMAD(pp. 239–243).
Khan, F. H., Qamar, U., & Bashir, S. (2019). Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approach. Soft Computing, 23(14), 5431–5442.CrossRef
Kuko, M., & Pourhomayoun, M. (2020). Single and clustered cervical cell classification with ensemble and deep learning methods. Information systems Frontiers, 1–13.
Lee, Y. J., Keeling, K. B., & Urbaczewski, A. (2019). The economic value of online user reviews with ad spending on movie box-office sales. Information Systems Frontiers, 21(4), 829–844.CrossRef
Li, B., Yang, Q., & Xue, X. (2009). Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th annual international conference on machine learning (pp. 617–624).
Li, F., Pan, S. J., Jin, O., Yang, Q., & Zhu, X. (2012a). Cross-domain co-extraction of sentiment and topic lexicons. In proceedings of the 50th annual meeting of the Association for Computational Linguistics: Long papers- (Vol. 1, pp. 410–419).
Li, L., Ye, W., Long, M., Tang, Y., Xu, J., & Wang, J. (2020). Simultaneous learning of pivots and representations for cross-domain sentiment classification. Association for the Advancement of Artificial Intelligence.
Li, T., Xiao, X., & Xue, Q. (2012b). An unsupervised approach for sentiment classification. In robotics and applications (ISRA), 2012 IEEE symposium (pp. 638–640).
Liang, P. W., & Dai, B. R. (2013). Opinion mining on social media data. In 2013 IEEE 14th International Conference on Mobile Data Management (Vol. 2, pp. 91–96). IEEE.
Meng, J., Long, Y., Yu, Y., Zhao, D., & Liu, S. (2019). Cross-domain text sentiment analysis based on CNN_FT method. Information, 10(5), 162.CrossRef
Mondal, T., Pramanik, P., Bhattacharya, I., Boral, N., & Ghosh, S. (2018). Analysis and early detection of rumors in a post disaster scenario. Information Systems Frontiers, 20(5), 961–979.CrossRef
Mostafa, L. (2020). Machine learning-based sentiment analysis for analyzing the travelers reviews on Egyptian hotels. In joint European-US workshop on applications of invariance in computer vision (pp. 405–413). Springer, Cham.
Niu, T., Zhu, S., Pang, L., & El Saddik, A. (2016). Sentiment analysis on multi-view social data. In international conference on multimedia modeling (pp. 15–27). Springer, Cham.
Olsher, D. J. (2012). Full spectrum opinion mining: Integrating domain, syntactic and lexical knowledge. IEEE 12th international conference on in data mining workshops (ICDMW), 2012 (pp. 693–700).
O'Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11(122–129), 1–2.
Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In LREc (Vol. 10, no. 2010, pp. 1320–1326).
Palshikar, G. K., Apte, M., & Pandita, D. (2018). Weakly supervised and online learning of word models for classification to detect disaster reporting tweets. Information Systems Frontiers, 20(5), 949–959.CrossRef
Pan, S. J., Ni, X., Sun, J. T., Yang, Q., & Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. In proceedings of the 19th international conference on world wide web (pp. 751–760). ACM.
Pang, B., Lee, L., &Vaithyanathan, S. (2002). Thumbs up?: Sentiment classification using machine learning techniques. In proceedings of the ACL-02 conference on empirical methods in natural language processing (Vol.10, pp. 79–86). Association for Computational Linguistics.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis (foundations and trends (R) in information retrieval. (Vol. 2, nos. 1/2, pp. 1–135).
Pang, B. (2006). Automatic analysis of document sentiment. Cornell University.
Peng, N., & Dredze, M. (2016). Multi-task multi-domain representation learning for sequence tagging. arXiv preprint arXiv:1608.02689.
Poria, S., Cambria, E., Howard, N., Huang, G. B., & Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174, 50–59.CrossRef
Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143–157.
Raaijmakers, S., & Kraaij, W. (2010). Classifier calibration for multi-domain sentiment classification. In Fourth International AAAI Conference on Weblogs and Social Media.
Shahheidari, S., Dong, H., & Daud, M. N. R. B. (2013). Twitter sentiment mining: A multi-domain analysis. Seventh international conference in complex, intelligent, and software intensive systems (CISIS), 2013 (pp. 144–149). IEEE.
Singh, P., Dwivedi, Y. K., Kahlon, K. S., Sawhney, R. S., Alalwan, A. A., & Rana, N. P. (2020). Smart monitoring and controlling of government policies using social media and cloud computing. Information Systems Frontiers, 22(2), 315–337.
Smith, K. S., McCreadie, R., Macdonald, C., & Ounis, I. (2018). Regional sentiment bias in social media reporting during crises. Information Systems Frontiers, 20(5), 1013–1025.CrossRef
Swain, A. K., & Cao, R. Q. (2017). Using sentiment analysis to improve supply chain intelligence. Information Systems Frontiers, 1–16.
Swamy, L. N., & Gorabal, J. V. (2020). Logistic regression-based classification for reviews analysis on E-commerce based applications. In Frontiers in intelligent computing: Theory and applications (pp. 323–334). Springer, Singapore.
Tackstrom, O., & McDonald, R. (2011). Semi-supervised latent variable models for sentence-level sentiment analysis. In proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human language technologies: Short papers- (Vol.2, pp. 569–574). Association for Computational Linguistics.
Täckström, Oscar., & McDonald, Ryan. (2011). Discovering fine-grained sentiment with latent variable structured prediction models. In European Conference on Information Retrieval (pp. 368–374). Springer, Berlin, Heidelberg
Tian, H., Chen, S. C., & Shyu, M. L. (2020). Evolutionary programming based deep learning feature selection and network construction for visual data classification. Information systems Frontiers, 1–14.
Titov, I. (2011). Domain adaptation by constraining inter-domain variability of latent feature representation. In proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human language technologies-volume 1 (Vol. 1, pp. 62–71). Association for Computational Linguistics.
Tran, T. U., Hoang, H. T. T., & Huynh, H. X. (2020). Bidirectional independently Long short-term memory and conditional random field integrated model for aspect extraction in sentiment analysis. In Frontiers in intelligent computing: Theory and applications (pp. 131–140). Springer, Singapore.
Troussas, C., & Virvou, M. (2020). Advances in social networking-based learning: Machine learning-based user Modelling and sentiment analysis (Vol. 181). Springer Nature. G-based user.
Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126.CrossRef
Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In proceedings of the 40th annual meeting on association for computational linguistics (pp. 417–424). Association for Computational Linguistics.
Wang, Y., & Li, B. (2015). Sentiment analysis for social media images. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 1584-1591). IEEE.
Wiebe, J (2000). Learning Subjective Adjectives from Corpora. In Aaai/iaai. 20(0), pp.0.
Wu, Y., Zhang, Q., Huang, X., & Wu, L. (2009). Phrase dependency parsing for opinion mining. In proceedings of the 2009 conference on empirical methods in natural language processing: (Vol.3-Vol.3, pp. 1533–1541). Association for Computational Linguistics.
Xu, Q., Chang, V., & Hsu, C. H. (2020). Event study and principal component analysis based on sentiment analysis–a combined methodology to study the stock market with an empirical study. Information systems Frontiers, 1–17.
Yan, L., & Zhang, Y. (2012). News sentiment analysis based on cross-domain sentiment word lists and content classifiers. In international conference on advanced data mining and applications (pp. 577–588). Springer, Berlin, Heidelberg.
Yin, H., Liu, P., Zhu, Z., Li, W., & Wang, Q. (2019). Capsule network with identifying transferable knowledge for cross-domain sentiment classification. IEEE Access, 7, 153171–153182.CrossRef
Yu, X., Liu, Y., Huang, X., & An, A. (2010). Mining online reviews for predicting sales performance: A case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering, 24(4), 720–734.
Zha, Z. J., Yu, J., Tang, J., Wang, M., & Chua, T. S. (2014). Product aspect ranking and its applications. IEEE Transactions on Knowledge and Data Engineering, 26(5), 1211–1224.CrossRef
Zhang, Y., Hu, X., Li, P., Li, L., & Wu, X. (2015). Cross-domain sentiment classification-feature divergence, polarity divergence or both? Pattern Recognition Letters, 65, 44–50.CrossRef
Zhao, C., Wang, S., & Li, D. (2020). Multi-source domain adaptation with joint learning for cross-domain sentiment classification. Knowledge-Based Systems, 191, 105254.CrossRef
Ziser, Y., & Reichart, R. (2018). Pivot based language modeling for improved neural domain adaptation. In proceedings of the 2018 conference of the north American chapter of the Association for Computational Linguistics: Human language technologies, volume 1 (pp. 1241–1251).
Ziser, Y., & Reichart, R. (2019). Task refinement learning for improved accuracy and stability of unsupervised domain adaptation. In proceedings of the 57th annual meeting of the Association for Computational Linguistics (pp. 5895–5906).
Metadaten
Titel
An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis
verfasst von
A. Geethapriya
S. Valli
Publikationsdatum
05.01.2021
Verlag
Springer US
Erschienen in
Information Systems Frontiers / Ausgabe 3/2021
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-020-10094-5

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