Combining sentiment analysis classifiers to explore multilingual news articles covering London 2012 and Rio 2016 Olympics

Abstract

This study aims to present an approach for the challenges of working with Sentiment Analysis (SA) applied to news articles in a multilingual corpus. It looks at the use and combination of multiple algorithms to explore news articles published in English and Portuguese. It presents a methodology that starts by evaluating and combining four SA algorithms (SenticNet, SentiStrength, Vader and BERT, being BERT trained in two datasets) to improve the quality of outputs. A thorough review of the algorithms’ limitations is conducted using SHAP, an explainable AI tool, resulting in a list of issues that researchers must consider before using SA to interpret texts. We propose a combination of the three best classifiers (Vader, Amazon BERT and Sent140 BERT) to identify contradictory results, improving the quality of the positive, neutral and negative labels assigned to the texts. Challenges with translation are addressed, indicating possible solutions for non-English corpora. As a case study, the method is applied to the study of the media coverage of London 2012 and Rio 2016 Olympic legacies. The combination of different classifiers has proved to be efficient, revealing the unbalance between the media coverage of London 2012, much more positive, and Rio 2016, more negative.

Understanding image-text relations and news values for multimodal news analysis

“…In addition, temporal reasoning and extracting temporal and causal relations can be beneficial to identify the impact and novelty of the event in news (Caselli and Vossen, 2017 ). Both lexical and neural network-based classifiers are well explored for sentiment and can be used to detect sentiment in news (Mello et al, 2022 ). Moreover, convolutional neural networks (CNN) and multimodal neural networks can also be applied for visual (Ortis et al, 2020 ) and multimodal (Soleymani et al, 2017 ) sentiment analysis.…”

“…Both lexical and neural networkbased classifiers are well explored for sentiment and can be Bidirectional encoder representations from transformers. used to detect sentiment in news (Mello et al, 2022). Moreover, convolutional neural networks (CNN) and multimodal neural networks can also be applied for visual (Ortis et al, 2020) and multimodal (Soleymani et al, 2017) sentiment analysis.…”

Mengukur Dampak Investasi Berkelanjutan: Evaluasi Terhadap Faktor ESG dalam Portofolio Investor

“…Analisis sentimen juga berperan dalam mengevaluasi efektivitas kebijakan pajak dan inisiatif pemerintah. Dengan menganalisis tren sentimen sebelum dan sesudah implementasi kebijakan, para peneliti dapat menilai apakah reformasi pajak telah mencapai tujuan yang diharapkan dan apakah reformasi pajak memiliki konsekuensi yang tidak diharapkan terhadap sentimen public (Lu et al, 2022;Mello et al, 2022).…”

تحليل السيميائية البصرية للصور والعاطفة في عناوين الصحف:

“…In sports too, a study by (Mello, Cheema, & Thakkar, 2022) study developed an approach for Sentiment Analysis (SA) applied to news articles in a multilingual corpus, using a combination of multiple algorithms to improve the quality of outputs. Additionally, the study aimed to address challenges with translation and provide solutions for non-English corpora.…”