Towards building a social emotion detection system for online news

Lei, Jingsheng; Rao, Yanghui; Li, Qing; Quan, Xiaojun; Wenyin, Liu

doi:10.1016/j.future.2013.09.024

Cited by 62 publications

(35 citation statements)

References 26 publications

(57 reference statements)

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“…It avoided manual feature engineering and used a real-valued word vectors which resulted in F-measures of 56.37 and 62.05, respectively. Lei et al [15] developed a system to predict emotions by generating emoticon and part of speech (POS) tagging on news articles. e accuracy was found to be 63.57%.…”

Section: Related Workmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

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Section: Related Workmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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“…But the emotions are a difficult concept to define; there are many results with different approaches, which hinders the accuracy of the identification and representation of these emotions by computers [4]. According to the Royal Spanish Academy emotion is defined as "intense and fleeting disturbance, pleasant or painful, which is accompanied by certain somatic shock", where "the mood" is interpreted as the perception people have of emotions, and "somatic shock" refers to physical manifestations that can generate human emotions; These two factors are essential in identifying emotions through the computer, where the affective computing derived from the Artificial Intelligence, whose main objective is to develop computational methods aimed at recognizing and generate human emotions [1], is an important aspect.…”

Section: Theoretical Aspects and Related Workmentioning

confidence: 99%

A Learning Object Recommendation Model with User Mood Characteristics

Méndez

Zapata

Collázos

2016

Social Computing in Digital Education

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Abstract. Emotions influence human cognition, affecting perception and understanding of a specific situation; therefore emotions can affect positively or negatively the learning process. Currently there are few information systems that analyze users' emotions to optimize their learning. This article proposes a model that includes users' temporary emotions to recommend Learning Objects (LO) and deliver relevant educational materials. Three stages are set; initially, the model recognizes a user's emotions and learning style; then a recommendation system is applied to identify relevant LOs; and finally, the presentation of this information is showed to the user. In this work we present a method that identifies the emotion of the user based on facial recognition, and the process of recommendation is presented.

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“…A social emotion detection for online news is described by Lei et al [10]. It performs the social emotion detection of online users in the online news domain.…”

Section: Related Work 31 Sentiment Analysismentioning

confidence: 99%

Sentiment and Emotion Analysis for Context Sensitive Information Retrieval of Social Networking Sites: A Survey

GeorgeAmalarethinam¹,

Nirmal²

2014

IJCA

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Context Sensitive Information Retrieval (CSIR) is quite a challenging issue because of the complexities involved in the process from semantics and ontology to the huge amount of processing capacity required to make it possible in real time. Understanding the semantic gap (where context is neglected) plays a major role in elimination false positives and improving the true positives in the information retrieval process. With big data becoming ubiquitous due to the volume, velocity and variety of data being presented and analysed in almost all the domains today, context sensitive analysis and interpretation of big data becomes important. This paper presents a comprehensive survey of the existing techniques for big data analysis based on massively parallel processing techniques like GPGPUs (CUDA), Hadoop MapReduce and also Data Warehousing. This paper presents a discussion about the datasets that are available for research and also the applications that could be thought of by context sensitive analysis of social media data. Also this paper provides research directions for context sensitive information retrieval and sentiment analysis in big data based on massively parallel processing architecture.

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Towards building a social emotion detection system for online news

Cited by 62 publications

References 26 publications

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

A Learning Object Recommendation Model with User Mood Characteristics

Sentiment and Emotion Analysis for Context Sensitive Information Retrieval of Social Networking Sites: A Survey

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