NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

Hamborg, Felix; Donnay, Karsten

doi:10.18653/v1/2021.eacl-main.142

Cited by 42 publications

(42 citation statements)

References 35 publications

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“…A number of studies have used seq2seq to classify the meaning of sentences [12]. They classify the articles' meaning whether they have positive meaning or negative.…”

Section: Related Workmentioning

confidence: 99%

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Similar Word Replacement Method for Improving News Commenter Analysis

Lee

Choi

2022

Applied Sciences

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In Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%.

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“…A number of studies have used seq2seq to classify the meaning of sentences [12]. They classify the articles' meaning whether they have positive meaning or negative.…”

Section: Related Workmentioning

confidence: 99%

“…They classify the articles' meaning whether they have positive meaning or negative. Hamborg et al introduced NewsMTSC, a high-quality dataset for target-dependent classification (TSC) of news articles [12].…”

Section: Related Workmentioning

confidence: 99%

Similar Word Replacement Method for Improving News Commenter Analysis

Lee

Choi

2022

Applied Sciences

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“…Thus, we seek to determine a fundamental effect resulting from framing: polarity of individual persons, which we identify on sentenceand aggregate to article-level. To achieve state of-the-art performance in target-dependent sentiment classification (TSC) on news articles, we use a fine-tuned RoBERTa-based neural model (𝐹 1 𝑚 = 83.1) [19].…”

Section: Systemmentioning

confidence: 99%

How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?

Hamborg¹,

Spinde²,

Heinser³

et al. 2021

Preprint

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Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left-and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.

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“…Sentiment analysis (also known as opinion mining) is the systematic identi cation, extraction, quanti cation, and study of affective states using natural language processing, text analysis, computational linguistics and biometrics via computer science analysis (Hamborg, 2021).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness

Caldo

Bologna²,

Nunzio³

et al. 2022

Preprint

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Background - Dynamic interplay between the patient collective consciousness and the subliminal affective content of digital information may play a critical role in emergence of chronic pain, within the combined perspective of somatic marker and complex adaptive system theoretical frames Goal - Testing Machine Learning (ML) algorithms accuracy to predictively discriminate back pain vs hip/knee osteoarthritis affective fingerprints of web pages Methods - Top 2000 internet pages related to the topics of interest were selected by relevance/popularity and submitted to automated sentiment analysis; Machine Learning algorithms classified the output Results - ML showed high discrimination accuracy predicting the page topic. The emotion Disgust emerged as the key discriminating factor Discussion - The new paradigm labeled “digital affective collective consciousness” (DACC) and the role of disgust in musculoskeletal disease are discussed; DACC may play a regulatory uninvestigated role of the emergence of health/illness conditions affecting subjects and communities

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NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

Cited by 42 publications

References 35 publications

Similar Word Replacement Method for Improving News Commenter Analysis

Similar Word Replacement Method for Improving News Commenter Analysis

How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?

Artificial Intelligence Discriminating Back Pain vs Hip/Knee Osteoarthritis within the Digital Affective Collective Consciousness

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