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Atkinson, J., & Escudero, A. (2022). Evolutionary natural-language coreference resolution for sentiment analysis. Int. J. Inf. Manage. Data Insights, 2(2), 100115.
Abstract: Communicating messages on social media usually conveys much implicit linguistic knowledge, which makes it difficult to process texts for further analysis. One of the major problems, the linguistic coreference resolution task involves detecting coreference chains of entities and pronouns that coreference them. It has mostly been addressed for formal and full-sized text in which a relatively clear discourse structure can be discovered, using Natural-Language Processing techniques. However, texts in social media are short, informal and lack a lot of underlying linguistic information to make decisions so traditional methods can not be applied. Furthermore, this may significantly impact the performance of several tasks on social media applications such as opinion mining, network analysis, sentiment analysis, text categorization. In order to deal with these issues, this research address the task of linguistic co-referencing using an evolutionary computation approach. It combines discourse coreference analysis techniques, domain-based heuristics (i.e., syntactic, semantic and world knowledge), graph representation methods, and evolutionary computation algorithms to resolving implicit co-referencing within informal opinion texts. Experiments were conducted to assess the ability of the model to find implicit referents on informal messages, showing the promise of our approach when compared to related methods.
Keywords: Natural-language processing; Sentiment analysis; Coreference resolution; Information management; Social media analysis; Evolutionary computation
Ruz, G. A., Henriquez, P. A., & Mascareno, A. (2020). Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Futur. Gener. Comp. Syst., 106, 92–104.
Abstract: Sentiment analysis through machine learning using Twitter data has become a popular topic in recent years. Here we address the problem of sentiment analysis during critical events such as natural disasters or social movements. We consider Bayesian network classifiers to perform sentiment analysis on two datasets in Spanish: the 2010 Chilean earthquake and the 2017 Catalan independence referendum. In order to automatically control the number of edges that are supported by the training examples in the Bayesian network classifier, we adopt a Bayes factor approach for this purpose, yielding more realistic networks. The results show the effectiveness of using the Bayes factor measure as well as its competitive predictive results when compared to support vector machines and random forests, given a sufficient number of training examples. Also, the resulting networks allow to identify the relations amongst words, offering interesting qualitative information to historically and socially comprehend the main features of the event dynamics. (C) 2020 Elsevier B.V. All rights reserved.
Keywords: Bayesian network classifiers; Twitter data; Sentiment analysis; Bayes factor; Support vector machines; Random forests