What emotions do news articles trigger in their readers?

Lin, Kevin; Yang, Changhua; Chen, Hsin-Hsi

doi:10.1145/1277741.1277882

Cited by 112 publications

(38 citation statements)

References 3 publications

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“…Several supervised and unsupervised classifiers have been built to recognize emotional content in texts [50][51]. The SNoW architecture [54] is one of the most useful frameworks for text-based emotion detection.…”

Section: Text: Affect Recognition From Textual Datamentioning

confidence: 99%

“…The SNoW architecture [54] is one of the most useful frameworks for text-based emotion detection. In the last decade, researchers have been focusing on emotion extraction from texts of different genres such as news [51], blogs [55], Twitter messages [56,57], and customer reviews [58]. Emotion extraction from social media content helps to predict the popularity of a product release or the results of an election poll, etc.…”

Section: Text: Affect Recognition From Textual Datamentioning

confidence: 99%

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Towards an intelligent framework for multimodal affective data analysis

et al. 2015

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An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate

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Section: Text: Affect Recognition From Textual Datamentioning

confidence: 99%

Section: Text: Affect Recognition From Textual Datamentioning

confidence: 99%

Towards an intelligent framework for multimodal affective data analysis

et al. 2015

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“…It has a rich set of applications such as tracking users' sentiments about products or events or about political candidates as expressed in online forums, customer relationship management, stock market prediction, social networking etc. Not only the classification of reviews [18] or newspaper articles [19] or blogs [20] but the Question-Answering systems [21] and current Information Retrieval systems [22] are also increasingly incorporating sentiment analysis within their scopes.…”

Section: Event-sentiment Taggingmentioning

confidence: 99%

Temporal Analysis of Sentiment Events – A Visual Realization and Tracking

Das

Kolya

Ekbal

et al. 2011

Computational Linguistics and Intelligent Text Processing

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Abstract. In recent years, extraction of temporal relations for events that express sentiments has drawn great attention of the Natural Language Processing (NLP) research communities. In this work, we propose a method that involves the association and contribution of sentiments in determining the event-event relations from texts. Firstly, we employ a machine learning approach based on Conditional Random Field (CRF) for solving the problem of Task C (identification of event-event relations) of TempEval-2007 within TimeML framework by considering sentiment as a feature of an event. Incorporating sentiment property, our system achieves the performance that is better than all the participated state-of-the-art systems of TempEval 2007. Evaluation results on the Task C test set yield the F-score values of 57.2% under the strict evaluation scheme and 58.6% under the relaxed evaluation scheme. The positive or negative coarse grained sentiments as well as the Ekman's six basic universal emotions (or, fine grained sentiments) are assigned to the events. Thereafter, we analyze the temporal relations between events in order to track the sentiment events. Representation of the temporal relations in a graph format shows the shallow visual realization path for tracking the sentiments over events. Manual evaluation of temporal relations of sentiment events identified in 20 documents sounds satisfactory from the purview of event-sentiment tracking.

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“…In the literature, there are recent studies about social emotion-related problems. For example, some works focus on sentiment analysis [24,35], social emotion analysis [6,13,18] of online documents and user emotion modeling [3]. However, few of them have paid attention to the dynamic characteristics of social emotions.…”

Section: Introductionmentioning

confidence: 99%

Tracking the evolution of social emotions with topic models

Zhu

et al. 2015

Knowl Inf Syst

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Many of today's online news Web sites have enabled users to specify different types of emotions (e.g., angry or shocked) they have after reading news. Compared with traditional user feedbacks such as comments and ratings, these specific emotion annotations are more accurate for expressing users' personal emotions. In this paper, we propose to exploit these users' emotion annotations for online news in order to track the evolution of emotions, which plays an important role in various online services. A critical challenge is how to model emotions with respect to time spans. To this end, we propose a timeaware topic modeling perspective for solving this problem. Specifically, we first develop two models named emotion-Topic over Time (eToT) and mixed emotion-Topic over Time (meToT), in which the topics of news are represented as a beta distribution over time and a multinomial distribution over emotions. While they can uncover the latent relationship among news, emotion and time directly, they cannot capture the evolution of topics. Therefore, we further develop another model named emotion-based Dynamic Topic Model (eDTM), where we explore the state space model for tracking the evolution of topics. In addition, we demonstrate that all of proposed models could enable several potential applications, such as emotion prediction, emotion-based news recommendations, and emotion anomaly detections. Finally, we validate the proposed models with extensive experiments with a real-world data set.

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What emotions do news articles trigger in their readers?

Cited by 112 publications

References 3 publications

Towards an intelligent framework for multimodal affective data analysis

Towards an intelligent framework for multimodal affective data analysis

Temporal Analysis of Sentiment Events – A Visual Realization and Tracking

Tracking the evolution of social emotions with topic models

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