Sentiment analysis on microblog utilizing appraisal theory

Korenek, Peter; Šimko, Marián

doi:10.1007/s11280-013-0247-z

Cited by 47 publications

(25 citation statements)

References 21 publications

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“…The sentiments in this case are reduced to the three classical opinions: positive, negative or neutral. In some other studies, fine-grained categories are added to have a more detailed analysis by using emotions such as (anger, disgust, fear, joy, sadness, and surprise) [24] or by adopting a linguistic theory such as appraisal [25], [20], [11] and [2].…”

Section: Multilingual Fine-granularity Sentiment Analysismentioning

confidence: 99%

A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

Abidi

Fohr

Jouvet

et al. 2019

Communications in Computer and Information Science

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The objective of this paper is to compare the opinions of two videos in two different languages. To do so, a fine-grained approach inspired from the appraisal theory is used to analyze the content of the videos that concern the same topic. In general, the methods devoted to sentiment analysis concern the study of the polarity of a text or an utterance. The appraisal approach goes further than the basic polarity sentiments and consider more detailed sentiments by covering additional attributes of opinions such as: Attitude, Graduation and Engagement. In order to achieve such a comparison, in AMIS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis.

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Section: Multilingual Fine-granularity Sentiment Analysismentioning

confidence: 99%

A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

Abidi

Fohr

Jouvet

et al. 2019

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…If a term contains such a negation (not, never), it is labelled as marked. According to Šimko and Korenek (2014), the main advantage of using the appraisal theory in sentiment classification is that it helps to take a look deeper inside the mind of authors who wrote texts and find out their real meaning using linguistic and psychological analysis of their texts. …”

Section: Theoretical Frameworkmentioning

confidence: 99%

A Systematic Approach to Terahertz-based Glucose Monitoring

Adibi¹,

Nguyen²,

Hamper³

et al. 2017

Digital Transformation – From Connecting Things to Transforming Our Lives

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“…So they presented an improved SVM classifier NBSVM which was better and more stable than SVM and Naïve Bayes. Korenek and Šimko [17] first created an appraisal dictionary by utilizing a psychological theory called appraisal theory which allowed a deeper and fine-grained analysis of microbloggings, followed by classifying posts using SVM classifier which revealed that the proposed method was feasible even for specific content presented on microbloggings. On the basis of common social network characteristics and other carefully generalized linguistic patterns, Li and Xu [18] proposed and implemented a novel method for identifying sentiment in microblogging posts.…”

Section: Related Workmentioning

confidence: 99%

A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

Wang

Zuo

Wang

2015

Computational Intelligence and Neuroscience

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Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

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Sentiment analysis on microblog utilizing appraisal theory

Cited by 47 publications

References 21 publications

A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

A Systematic Approach to Terahertz-based Glucose Monitoring

A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

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