Sentiment Analysis in the News

By A. Balahur, Ralf Steinberger, Mijail Alexandrov Kabadjov, Vanni Zavarella, E. V. D. Goot, Matina Halkia, Bruno Pouliquen, Jenya Belyaeva. International Conference on Language Resources and Evaluation, 2010

324 citations recorded by the source corpus.

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles ― author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which we apply these concepts. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/996978a9fe73a34906f2187301f0a0334cced862

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In Artificial Intelligence

Citations in the corpus

"Happy and Assured that life will be easy 10years from now.": Perceptions of Artificial Intelligence in 8 Countries (2019, background): “At the same time, sentiment analysis of newspaper articles from the New York Times and associated content found that, in general, AI has had consistently more optimistic than pessimistic coverage over time [21], and did not support the hypothesis that news media coverage of AI is negative [25].”
Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards (2015, background): “For example, image tagging and sentiment analysis may also represent cultural-specific knowledge tasks; previous research has pointed to cultural differences as a limiting factor in the accuracy of sentiment analysis [2] and it has shown that image tags can vary across national cultures [11].”
Debiasing Word Embeddings from Sentiment Associations in Names (2020, methodology): “[2], we extracted a sample of quotation phrases from our news collection containing at least one name and let crowdworkers decide if the phrases are positive, negative, or neutral, using majority voting with three judgments per phrase.”
Sentiment strength detection for the social web (2012, background): “This could particularly impact on sentiment analysis for news (Balahur et al., 2010) or politics (Balahur, Kozareva, & Montoyo, 2009).”
Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning (2019, background): “In regards to biases originating from crowd work, several prior works have looked at biases introduced by the process of labeling via crowds (e.g., [3, 5, 6, 8, 9, 23, 24, 26, 34]).”
Measuring objective and subjective well-being: dimensions and data sources (2020, background): “, opposing politicians) [164] and are continuously updated, containing even archived historical news of the last decades.”
Author’s Sentiment Prediction (2020, background): “Sentiment analysis in news articles has been studied from various perspectives for different types of applications (Yu et al., 2013; Ahmad et al., 2007; Godbole et al., 2007; Balahur et al., 2013).”
Personalized news recommendation using classified keywords to capture user preference (2014, methodology): “Recently, sentiment analysis [8] is often used for analysis of social networks.”