A novel approach based on an extended cuckoo search algorithm for the classification of tweets which contain Emoticon and Emoji

Redmond, Molly C.; Salesi, Sadegh; Cosma, Georgina

doi:10.1109/ickea.2017.8169894

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…They are different in that the former needs a human annotated corpus while the latter doesn't. The effectiveness of using emoji as a way of training classifiers has been proven (Hallsmar and Palm, 2016) and furthermore it has been shown that emoji outperform emoticons (Redmond et al, 2017). An example of supervised learning is the emoticon smoothed language model (ESLAM) proposed by Liu et al (2012), which classifies twitter based on a model trained by a human annotated corpus.…”

Section: Research Fields Regarding Emojimentioning

confidence: 99%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.

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Section: Research Fields Regarding Emojimentioning

confidence: 99%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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“…Again, coupled with SVM, Cuckoo search has been adopted in FS to deal with problem of phishing mail detection [127]. Recently, extended versions of Cuckoo Search algorithm have been advanced to cope with classification of tweets in sentiment analysis [128], or to defeat attacks in Software Defined Network infrastructures [129].…”

Section: Cuckoo Searchmentioning

confidence: 99%

Supervised feature selection techniques in network intrusion detection: A critical review

Mauro

Galatro²,

Fortino

et al. 2021

Engineering Applications of Artificial Intelligence

103

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Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial.

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“…In addition to emotional words, netizens often express their emotions via other symbols, such as emoticons, slangs, and hashtags. Researchers have widely discussed the role of these features in identifying the emotions of texts on Twitter [57][58][59][60][61][62][63]. Among them, emoticons, which have clear emotional meanings, are often widely used by netizens in microblog environments to express their feelings [64][65][66].…”

Section: Plos Onementioning

confidence: 99%

Identifying critical outbreak time window of controversial events based on sentiment analysis

Wang

Zhang

et al. 2020

PLoS ONE

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The response of netizens toward controversial events plays an important guiding role in the development of events. Based on the identification of such responses, this study aimed to determine the critical outbreak time window of events. The microblog texts related to an event were divided into seven emotional categories via multi-emotional analysis to capture the subtle emotions of netizens toward an event, i.e., public opinion. By detecting the characteristics of the text and regional coverage of emotions, an emotional coverage index that reflects the intensity of emotional impact was proposed to determine the mainstream emotion of netizens. By capturing the mutation characteristics of the impact intensity of mainstream emotions, the critical time window of the public opinion toward the event was obtained. The experimental results demonstrated that the proposed method can effectively identify the critical outbreak time window of controversial events, which can help authorities in preventing the further aggravation of events.

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A novel approach based on an extended cuckoo search algorithm for the classification of tweets which contain Emoticon and Emoji

Cited by 15 publications

References 14 publications

A Systematic Review of Emoji: Current Research and Future Perspectives

A Systematic Review of Emoji: Current Research and Future Perspectives

Supervised feature selection techniques in network intrusion detection: A critical review

Identifying critical outbreak time window of controversial events based on sentiment analysis

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