Sentiment and emotional analysis of risk perception in the Herculaneum Archaeological Park during Covid-19 pandemic

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).This paper proposes a methodology for sentiment analysis with emphasis on the emotional aspects of people visiting the Herculaneum Archaeological Park. The methodology provides a valuable means of continuous feedback on perceived risk of the site. A semantic analysis on Twitter text messages provides input to the risk management team with which they can respond immediately mitigating any apparent risk and reducing the perceived risk. In addition, we analyse the sentiments of people of such a cultural heritage before and during the Covid pandemic. Whilst suffering from the disease, equally people suffered due to loneliness from isolation as declared by the World Health Organisation. Despite such conditions, people’s sentiments demonstrated a positive effect from the online discussions on the Herculaneum site. This is what we have demonstrated in this work through sentiment analysis.Peer reviewe

a digital humanities platform to explore the Portuguese cultural heritage

LISBOA-01-0145-FEDER-022139The ROSSIO Infrastructure is developing a free and open-access platform for aggregating, organising, and connecting the digital resources in the Social Sciences, Arts and Humanities provided by Portuguese higher education and cultural institutions. This paper presents an overview of the ROSSIO Infrastructure, its main objectives, the institutions involved, and the services offered by the infrastructure’s aims through its platform—namely, a discovery portal, digital exhibitions, collections, and a virtual research environment. These services rely on a metadata-aggregation solution for bringing the digital objects’ metadata from the providing institutions into ROSSIO. The aggregated datasets are converted into linked data and undergo an enrichment process based on controlled vocabularies, which are developed and published by ROSSIO. The paper will describe this process, the applications involved, and how they interoperate. We will further reflect on how these services may enhance the dissemination of science, considering the FAIR principles.publishersversionpublishe

Indirectly Named Entity Recognition

[EN] We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis.  While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.This research has been funded by the FEDER (Fonds européen de développement régional) and selected by the French-Swiss programme Interreg V. We would like to thank Claire Wuillemin for her preliminary work in the DecRIPT project about the State-of-the-Art in NER and SER in 2020. We would also like to thank for their advice Gilles Falquet, Luka Nerima, Eric Wehrli and Jean-Philippe Goldman at the University of Geneva.Kauffmann, A.; Rey, F.; Atanassova, I.; Gaudinat, A.; Greenfield, P.; Madinier, H.; Cardey, S. (2021). Indirectly Named Entity Recognition. Journal of Computer-Assisted Linguistic Research. 5(1):27-46. https://doi.org/10.4995/jclr.2021.15922OJS274651Abney, Steven. 1987. "The English Noun Phrase in its Sentential Aspect." PhD diss., Massachusetts Institute of Technology.Alsharaf, H., S. Cardey, P. Greenfield, D. Limame, and I. Skouratov. 2003. "Fixedness, the complexity and fragility of the phenomenon: some solutions for natural language processing." In Proceedings of ICL17. Prague, Czech Republic: Matfyzpress.Ananthanarayanan, Rema, Vijil Chenthamarakshan, Prasad M Deshpande, and Raghuram Krishnapuram. 2008. "Rule Based Synonyms for Entity Extraction from Noisy Text." In Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data AND '08, 31-38. Singapore: Association for Computing Machinery. https://doi.org/10.1145/1390749.1390756Bachellier, Jean-Louis. 1972. "Sur-Nom." Le texte: de la théorie à la recherche, no. 19: 69-92. doi :10.3406/comm.1972.1283. https://doi.org/10.3406/comm.1972.1283Baldwin, Timothy, and Su Nam Kim. 2013. "Multiword Expressions." In Handbook of Natural Language Processing, Second Edition, edited by Nitin Indurkhya and Fred J. Damerau, 267-292. Boca Raton, USA: CRCPress.Bohn, C., and Kjeti Nørvag. 2010. "Extracting Named Entities and Synonyms from Wikipedia." In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, 1300-1307. https://doi.org/10.1109/AINA.2010.50Cai, Desheng, and Gongqing Wu. 2019. "Content-aware attributed entity embedding for synonymous named entity discovery." Neurocomputing 329: 237-247. https://doi.org/10.1016/j.neucom.2018.10.055Chakrabarti, K., S. Chaudhuri, T. Cheng, and Dong Xin. 2012. "A framework for robust discovery of entity synonyms." In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1384-1392, Beijing, China: Association for Computing Machinery. https://doi.org/10.1145/2339530.2339743Charton, Eric, Michel Gagnon, and Benoit Ozell. 2011. "Génération automatique de motifs de détection d'entités nommées en utilisant des contenus encyclopédiques (Automatic generation of named entity detection patterns using encyclopedic contents)" [in French]. In Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs, 13-24. Montpellier, France: ATALA.Cho, Hyejin, Wonjun Choi, and Hyunju Lee. 2017. "A method for named entity normalization in biomedical articles: application to diseases and plants." BMC bioinformatics 18, no. 1 ( 1-12. https://doi.org/10.1186/s12859-017-1857-8Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186. Minneapolis, Minnesota: Association for Computational Linguistics.Friburger, Nathalie. 2006. "Linguistique et reconnaissance automatique des noms propres." Meta 51, no. 4: 637-650. doi:10.7202/014331ar. https://doi.org/10.7202/014331arGuenoune, Hani, Kevin Cousot, Mathieu Lafourcade, Melissa Mekaoui, and Cédric Lopez. 2020. "A Dataset for Anaphora Analysis in French Emails." In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, 165-175. Barcelona, Spain (online): Association for Computational Linguistics.Honnibal, Matthew, and Ines Montani. 2017. "spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing."Kampeera, Wannachai, and Sylviane Cardey-Greenfield. 2012. "Building a Lexically and Semantically-Rich Resource for Paraphrase Processing." In Advances in Natural Language Processing, edited by Hitoshi Isahara and Kyoko Kanzaki, 138-143. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_14Kauffmann, Alexis. 2013. "Structural Asymmetries in Machine Translation: The case of English-Japanese". PhD diss., Université de Genève. https://doi.org/10.13097/archive-ouverte/unige:34540.Lample, Guillaume, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. "Neural Architectures for Named Entity Recognition." In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 260-270. San Diego, California: Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1030Lin, Bill Yuchen, Dong-Ho Lee, M. Shen, Ryan Rene Moreno, X. Huang, Prashant Shiralkar, and X. Ren. 2020. "TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8503-8511. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.752Lopez, C., Melissa Mekaoui, K. Aubry, Jean Bort, and Philippe Garnier. 2019. "Reconnaissance d'entités nommées itérative sur une structure en dépendances syntaxiques avec l'ontologie NERD." Revue des Nouvelles Technologies de l'Information, Extraction et Gestion des connaissances, RNTI-E-35, 81-92.Ma, Jie, Jun Liu, Y. Li, X. Hu, Yudai Pan, S. Sun, and Qika Lin. 2020. "Jointly Optimized Neural Coreference Resolution with Mutual Attention." In Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, Texas, USA: Association for Computing Machinery. https://doi.org/10.1145/3336191.3371787Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. Baltimore, Maryland: Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-5010Martin, Louis, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Benoıt Sagot, and Djamé Seddah. 2020. "Les modèles de langue contextuels CamemBERT pour le français: impact de la taille et de l'hétérogénéité des données d'entrainement (CamemBERT Contextual Language Models for French: Impact of Training Data Size and Heterogeneity)" [in French]. In Actes de la 6e conférence conjointe Journées d'Etudes sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Etudiants Chercheurs en Informatique pour le' Traitement Automatique des Langues (RECITAL, 22e édition). Volume 2: Traitement Automatique des Langues Naturelles, 54-65. Nancy, France: ATALA et AFCP.Mitkov, Ruslan. 2014. Anaphora resolution. Routledge. https://doi.org/10.4324/9781315840086Mohamed, Muhidin A., and Mourad Chabane Oussalah. 2020. "A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics." Language Resources and Evaluation 54 : 457-485. https://doi.org/10.1007/s10579-019-09466-4Nadeau, David, and Satoshi Sekine. 2007. "A survey of named entity recognition and classification." Lingvisticae Investigationes 30: 3-26. https://doi.org/10.1075/li.30.1.03nadNayel, Hamada A., H. L. Shashirekha, Hiroyuki Shindo, and Yuji Matsumoto. 2019. "Improving Multi-Word Entity Recognition for Biomedical Texts." CoRRabs/1908.05691. arXiv:1908.05691.Nebhi, Kamel. 2013. "Named Entity Disambiguation using Freebase and Syntactic Parsing." In [email protected], Damien, Maud Ehrmann, and Sophie Rosset. 2016. "Evaluating Named Entity Recognition." Chap. 6 in Named Entities for Computational Linguistics, 111-129. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119268567.ch6Ortiz Suarez, Pedro Javier, Yoann Dupont, Benjamin Muller, Laurent Romary, and Benoıt Sagot. 2020. "Establishing a New State-of-the-Art for French Named Entity Recognition" [in English]. In Proceedings of the 12th Language Resources and Evaluation Conference, 4631-4638. Marseille, France: European Language Resources Association.Petit, Gérard. 2006. "Le nom de marque déposée : nom propre, nom commun et terme." Meta 51, no. 4: 690-705. doi:10.7202/014335ar. https://doi.org/10.7202/014335arQu, Meng, Xiang Ren, and Jiawei Han. 2017. "Automatic Synonym Discovery with Knowledge Bases." In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 997-1005. KDD '17. Halifax, NS, Canada: Association for Computing Machinery. https://doi.org/10.1145/3097983.3098185Racicot, André. 2009. "Traduire le monde: Venise du Nord et autres surnoms." L'Actualité langagière, vol. 6, n° 2, 23. Travaux publics et Services gouvernementaux Canada.Rey, François-Claude, and Kauffmann Alexis. 2021. "French indirectly named entities (version 1.3) [Data set]." Zenodo. https://doi.org/10.5281/zenodo.5158253.Rosales-Méndez, Henry, Aidan Hogan, and Barbara Poblete. 2019. "Fine-Grained Evaluation for Entity Linking." 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Ensemble deep learning for multilabel binary classification of user-generated content

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5% to 5.4%

Event extraction and representation: A case study for the portuguese language

Text information extraction is an important natural language processing (NLP) task, which aims to automatically identify, extract, and represent information from text. In this context, event extraction plays a relevant role, allowing actions, agents, objects, places, and time periods to be identified and represented. The extracted information can be represented by specialized ontologies, supporting knowledge-based reasoning and inference processes. In this work, we will describe, in detail, our proposal for event extraction from Portuguese documents. The proposed approach is based on a pipeline of specialized natural language processing tools; namely, a part-of-speech tagger, a named entities recognizer, a dependency parser, semantic role labeling, and a knowledge extraction module. The architecture is language-independent, but its modules are language-dependent and can be built using adequate AI (i.e., rule-based or machine learning) methodologies. The developed system was evaluated with a corpus of Portuguese texts and the obtained results are presented and analysed. The current limitations and future work are discussed in detail

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.©2022 SAGE Publications. The article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference.fi=vertaisarvioitu|en=peerReviewed

Network alignment across social networks using multiple embedding techniques

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10-15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.Web of Science1021art. no. 397

Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain

Open Data Quality Evaluation: A Comparative Analysis of Open Data in Latvia

Nowadays open data is entering the mainstream - it is free available for every stakeholder and is often used in business decision-making. It is important to be sure data is trustable and error-free as its quality problems can lead to huge losses. The research discusses how (open) data quality could be assessed. It also covers main points which should be considered developing a data quality management solution. One specific approach is applied to several Latvian open data sets. The research provides a step-by-step open data sets analysis guide and summarizes its results. It is also shown there could exist differences in data quality depending on data supplier (centralized and decentralized data releases) and, unfortunately, trustable data supplier cannot guarantee data quality problems absence. There are also underlined common data quality problems detected not only in Latvian open data but also in open data of 3 European countries.Comment: 24 pages, 2 tables, 3 figures, Baltic J. Modern Computin