Sentiment Analysis in the Rest-Mex Challenge
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13613 LNAI, Page: 137-147
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In this paper, we describe our participation in the Rest-Mex 2022 forum for the Sentiment Analysis task. The objective of the task was to create a model capable of predicting the polarity of the sentiment expressed by a tourist’s opinion, as well as the type of attraction visited. For this task, we followed two different approaches: a lexicon-based approach and a Machine Learning approach. In the lexicon-based approach, we use a dictionary with words that have a numerical value that specifies the association with some emotions or attractions. We trained a logistic regression model for the Machine Learning approach to predict sentiment polarity and attractions. Our proposal obtained a combined score for both tasks of 0.85, which is only 0.03 away from the best reported result.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85142853541&origin=inward; http://dx.doi.org/10.1007/978-3-031-19496-2_11; https://link.springer.com/10.1007/978-3-031-19496-2_11; https://dx.doi.org/10.1007/978-3-031-19496-2_11; https://link.springer.com/chapter/10.1007/978-3-031-19496-2_11
Springer Science and Business Media LLC
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