Exploring Implicit Sentiment Evoked by Fine-grained News Events (2021)

Abstract: We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content diversity, which is one way to mitigate the detrimental effects of filter bubbles that recommenders based on collaborative filtering may produce. Our experiments are based on 1,735 news articles from major Flemish newspapers that were manually annotated, with high agreement, for implicit sentiment. While lexical resources prove insufficient for sentiment analysis in this data genre, our results demonstrate that machine learning models based on SVM and BERT are able to automatically infer the implicit sentiment evoked by news events.

## introduction why do we read the news that we read and how are news articles received by their audiences? both research questions are important in the domains of news personalization, framing theory and sentiment and emotion analysis, among others. digitization and globalization have profoundly changed the media ecology @xcite . there is an increasing trend to consume news via the internet (54%, as opposed to 22% consuming print media foot_0 ), and more specifically via newspaper websites, smartphone apps, social media, etc. this (partial) shift to online news consumption assigns much more responsibility to citizens, who select from a wide variety of news sources, distributors and topics. recommendation algorithms do part of the work by filtering, out of the extensive offer of information, news that sparks citizens' interest. most commonly, such algorithms apply collaborative filtering, which is based on users' past reading behaviour and similar interests in their network. a detrimental side effect of this interplay between algorithms and user behaviour, especially on social media platforms, is that it may lead to a less diverse news consumption, a phenomenon often referred to as the 'filter bubble' @xcite . a game changer in this respect are algorithms that use content diversity as the key driver for personalized news recommendation. to date, however, content-based filtering is largely based on topic clustering and keyword matching @xcite without considering semantic information including sentiment and controversy. the present study is part of the #news-dna project which aims to investigate and develop a news recommendation algorithm that is driven by content-based diversity foot_2 . however, before implementing this type of diversity into a recommender, we need to be able to automatically derive sentiment from newswire text. to this end, we explore whether news events evoke implicit sentiment in the reader and, if so, whether this implicit sentiment can be derived automatically using lexicon-based and machine learning techniques. we focus on text spans that describe hard news events (i.e. covering important topics in the public debate, such as politics, finance and economics, war and crime, as well as international news @xcite @xcite ). this paper is the first initiative to model the semantics of written editorial content (devoid of topic restrictions), where fine-grained news events' implicit sentiment is examined manually, and where attempts are made to model this sentiment automatically. besides presenting a novel dataset for implicit sentiment detection in news texts, we aim to answer the following research question: compared to the high number of sentiment prediction pipelines that have been established for ugc analysis @xcite , not a great deal of research has been done into sentiment analysis at a fine-grained (i.e. below the sentence) level, sentiment analysis in factual data, multi-modal data or in figurative language like irony and humour, etc. @xcite . with this paper, we aim to tackle two of the above-mentioned challenges simultaneously by predicting implicit sentiment evoked by fine-grained (factual) news events. sentiment analysis has a broader application range than detecting explicit sentiment clues in subjective texts. objective utterances can express sentiment as well, be it indirectly by either specific language use (i.e. words that activate emotional values), or by the sentiment certain events evoke through cultural or personal emotional connection. this distinction brings up the terminological confusion around sentiment and opinion. as pointed out by @xcite , the difference between the two is quite subtle, but dictionary definitions of both terms indicate that opinions represent a person's concrete view, whereas sentiments are more of a person's feeling. although both are not completely independent of one another, it is worthwhile to mention this distinction so as to have a good understanding of the related research. implicit sentiment can thus be analyzed from the author's perspective (i.e. implicit opinions), as well as from the reader's (i.e. implicit sentiment). research on implied opinions is prevalent in research areas such as electoral politics (e.g. @xcite , political viewpoints and argumentation mining (e.g. @xcite and stock market predictions (e.g. @xcite , but it is also gaining research interest in typical ugc analysis, for instance to detect irony and sarcasm (e.g. @xcite , and for analyzing newswire text. looking at the mere impact of news events on their audiences without having readers' reactions at hand, the focus of this research lies on detecting implicit sentiment rather than implied opinions. irrespective of potential framing, when consuming news, readers may infer a positive or negative impression of an event or topic based on world knowledge, cultural background, historical context or even personal experiences. such text spans are known as "statements or phrases that describe positive or negative factual information about something without conveying a private state" @xcite . later, @xcite coined the term 'polar facts' to refer to such statements. in what follows, we discuss some seminal studies on sentiment analysis in factual text from both the author's and readers' perspectives. ## corpus construction striving for diversification driven by content analysis, our research focus is on fine-grained news events (see section 1), and more specifically the implicit sentiment they evoke in the reader. in the following paragraphs, we zoom in on the data collection and annotation process and present the results of inter-rater-agreement experiments. ## corpus analysis and experiments in the following paragraphs, we thoroughly investigate our research question by analyzing the full corpus and conducting experiments to examine whether detecting implicit sentiment evoked by news events is a feasible task. ## conclusion and future work with this paper, we investigated the detection of implicit sentiment evoked by dutch newswire text. while related research approaches the task mainly at the document or sentence level using lexiconbased methods, we focused on fine-grained events below the sentence level and experimented with lexicon-based approaches and machine learning. for the latter, we compared the performance of svms, which have proven successful in sentiment analysis tasks, with two transformer-based models. our results demonstrate that the machine learning approach performs accurately with a top f 1 score of 0.72 and shows a considerable improvement over the majority baseline. the experiments also demonstrate that machine learning clearly outperforms the lexicon-based approach, even when extensive (implicit) sentiment lexicons are used. furthermore, we created and manually annotated a dutch corpus of news events and were able to show high inter-rater agreement for event sentiment and sentiment span annotations. in future research, it will be interesting to explore whether additional context, including named entities and co-referring events, inside and across sentence boundaries, can improve implicit sentiment detection further.