{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:05:38Z","timestamp":1777039538670,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030491604","type":"print"},{"value":"9783030491611","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-49161-1_34","type":"book-chapter","created":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T08:04:41Z","timestamp":1590739481000},"page":"409-418","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["On the Reusability of Sentiment Analysis Datasets in Applications with Dissimilar Contexts"],"prefix":"10.1007","author":[{"given":"S.","family":"Sarlis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-399X","authenticated-orcid":false,"given":"I.","family":"Maglogiannis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"34_CR1","unstructured":"Angiani, G., et al.: A comparison between preprocessing techniques for sentiment analysis in Twitter. In: KDWeb (2016)"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)","DOI":"10.1609\/aaai.v28i1.8928"},{"key":"34_CR3","unstructured":"Younis, E.M.G.: Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study. Int. J. Comput. Appl. 112(5), 44\u201348 (2015)"},{"key":"34_CR4","unstructured":"Lopez, B., Minh, A.N., Xavier, S.: IMDb sentiment analysis. COMP 551 - Group 17 (2019)"},{"key":"34_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46681-1","volume-title":"Neural Information Processing","year":"2016","unstructured":"Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.): ICONIP 2016. LNCS, vol. 9950. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46681-1"},{"key":"34_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-76941-7","volume-title":"Advances in Information Retrieval","year":"2018","unstructured":"Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.): ECIR 2018. LNCS, vol. 10772. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76941-7"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Camacho-Collados, J., Pilehvar, M.T.: On the role of text preprocessing in neural network architectures: an evaluation study on text categorization and sentiment analysis. arXiv preprint arXiv:1707.01780 (2017)","DOI":"10.18653\/v1\/W18-5406"},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1613\/jair.1.11259","volume":"63","author":"J Camacho-Collados","year":"2018","unstructured":"Camacho-Collados, J., Pilehvar, M.T.: From word to sense embeddings: a survey on vector representations of meaning. J. Artif. Intell. Res. 63, 743\u2013788 (2018)","journal-title":"J. Artif. Intell. Res."},{"issue":"12","key":"34_CR9","doi-asserted-by":"publisher","first-page":"37","DOI":"10.23956\/ijermt.v6i12.32","volume":"6","author":"Bhagyashri Wagh","year":"2018","unstructured":"Wagh, B., Shinde, J.V., Kale, P.A.: A Twitter sentiment analysis using NLTK and machine learning techniques. Int. J. Emerg. Res. Manag. Technol. 6(12), 37\u201344 (2017)","journal-title":"International Journal of Emerging Research in Management and Technology"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Martineau, J.C., Finin, T.: Delta TFIDF: an improved feature space for sentiment analysis. In: Third International AAAI Conference on Weblogs and Social Media (2009)","DOI":"10.1609\/icwsm.v3i1.13979"},{"key":"34_CR11","first-page":"354","volume":"4","author":"M Toman","year":"2006","unstructured":"Toman, M., Tesar, R., Jezek, K.: Influence of word normalization on text classification. Proc. InSciT 4, 354\u2013358 (2006)","journal-title":"Proc. InSciT"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Aisopos, F., Papadakis, G., Varvarigou, T.: Sentiment analysis of social media content using N-Gram graphs. In: Proceedings of the 3rd ACM SIGMM International Workshop on Social Media (2011)","DOI":"10.1145\/2072609.2072614"},{"key":"34_CR13","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-981-13-1501-5_41","volume-title":"Emerging Technologies in Data Mining and Information Security","author":"M Avinash","year":"2019","unstructured":"Avinash, M., Sivasankar, E.: A study of feature extraction techniques for sentiment analysis. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security, vol. 814, pp. 475\u2013486. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-1501-5_41"},{"key":"34_CR14","unstructured":"Tarimer, \u0130., \u00c7oban, A., Kocaman, A.E.: Sentiment analysis on IMDB movie comments and Twitter data by machine learning and vector space techniques. arXiv preprint arXiv:1903.11983 (2019)"},{"key":"34_CR15","unstructured":"Pelaez, A., Talal, A., Mohsen, G.: Sentiment analysis of IMDb movie. Mach. Learn. 198(536) (2015)"},{"key":"34_CR16","unstructured":"Oswal, N.: Predicting rainfall using machine learning techniques. arXiv preprint arXiv:1910.13827 (2019)"},{"key":"34_CR17","unstructured":"Nicapotato: Women\u2019s E-Commerce Clothing Reviews. Kaggle, 3 February 2018. www.kaggle.com\/nicapotato\/womens-ecommerce-clothing-reviews"},{"key":"34_CR18","unstructured":"Stanford Network Analysis Project: Amazon Fine Food Reviews. Kaggle, 1 May 2017. www.kaggle.com\/snap\/amazon-fine-food-reviews"},{"key":"34_CR19","unstructured":"Datafiniti: Hotel Reviews. Kaggle, 24 June 2019. www.kaggle.com\/datafiniti\/hotel-reviews"},{"key":"34_CR20","unstructured":"Datafiniti: Consumer Reviews of Amazon Products. Kaggle, 20 May 2019. www.kaggle.com\/datafiniti\/consumer-reviews-of-amazon-products"},{"key":"34_CR21","unstructured":"Youben: Twitter Sentiment Analysis. Kaggle, 21 October 2018. www.kaggle.com\/youben\/twitter-sentiment-analysis\/data"},{"key":"34_CR22","unstructured":"mistryjimit26: Twitter Sentiment Analysis Basic. Kaggle, 21 September 2018. www.kaggle.com\/mistryjimit26\/twitter-sentiment-analysis-basic\/data"},{"key":"34_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1007\/978-3-540-30549-1_43","volume-title":"AI 2004: Advances in Artificial Intelligence","author":"AM Kibriya","year":"2004","unstructured":"Kibriya, A.M., Frank, E., Pfahringer, B., Holmes, G.: Multinomial Naive Bayes for text categorization revisited. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 488\u2013499. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30549-1_43"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Thongtan, T., Phienthrakul, T.: Sentiment classification using document embeddings trained with cosine similarity. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (2019)","DOI":"10.18653\/v1\/P19-2057"},{"key":"34_CR25","unstructured":"Agarwal, A., et al.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011) (2011)"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-49161-1_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T23:16:22Z","timestamp":1716938182000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-49161-1_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030491604","9783030491611"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-49161-1_34","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Neos Marmaras","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aiai2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"149","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}