Emoji Helps! A Multi-modal Siamese Architecture for Tweet User Verification

Suman, Chanchal; Saha, Sriparna; Bhattacharyya, Pushpak; Chaudhari, Rohit

doi:10.1007/s12559-020-09715-7

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…We also normalize the contractions in utterances with contractions [37] library and use Spacy [4] for lemmatization. Following previous work [10,71], we replace the emojis with specific strings to standard ASCII strings. Besides, we detect low frequency tokens such as URL, email address, code, HTML tag, and version number with regular expressions, and substitute them into [URL], [EMAIL], [HTML], [CODE], and [VERSION] respectively.…”

Section: Preprocessing and Disentanglementmentioning

confidence: 99%

BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

Shi¹,

Mu²,

Yang³

et al. 2022

Preprint

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In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural network to learn the contextual information; 3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average F1 of 77.74%, improving the best baseline by 12.96%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%, 12.21%, 10.91%, respectively. A human evaluation study also confirms the effectiveness of BugListener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.

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Section: Preprocessing and Disentanglementmentioning

confidence: 99%

BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

Shi¹,

Mu²,

Yang³

et al. 2022

Preprint

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show abstract

“…RQ49. Tracking and investigating the usage of multiple emojis expressed in the tweets related to different use cases of exoskeletons for investigating the associated sentiment [123,124], performing tweet classification [125], user verification [126], irony detection [127], and trust modeling [128]. RQ50.…”

Section: Rq4mentioning

confidence: 99%

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Thakur¹

2022

Preprint

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The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

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“…An encoderdecoder model named as Seq2Emoji was used to predict multiple emojis based on a short text, considering the correlations between emojis and predicting emoji after the fully connected layer of hierarchical neural networks [37]. The features of given texts and emojis could be automatically extracted by a multi-modal Siamese-based framework [38]. To represent the conceptual content through emoji from cognitive strategies, participants were asked to use emoji to provide semantic representations for abstract and concrete concepts [39].…”

Section: Emoji In Sentiment Analysismentioning

confidence: 99%

A multi-modal and multi-scale emotion-enhanced inference model based on fuzzy recognition

Qiu

Yan

2021

Complex Intell. Syst.

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Only the label corresponding to the maximum value of the fully connected layer is used as the output category when a neural network performs classification tasks. When the maximum value of the fully connected layer is close to the sub-maximum value, the classification obtained by considering only the maximum value and ignoring the sub-maximum value is not completely accurate. To reduce the noise and improve classification accuracy, combining the principles of fuzzy reasoning, this paper integrates all the output results of the fully connected layer with the emotional tendency of the text based on the dictionary to establish a multi-modal fuzzy recognition emotion enhancement model. The provided model considers the enhancement effect of negative words, degree adverbs, exclamation marks, and question marks based on the smallest subtree on the emotion of emotional words, and defines the global emotional membership function of emojis based on the corpus. Through comparing the results of CNN, LSTM, BiLSTM and GRU on Weibo and Douyin, it is shown that the provided model can effectively improve the text emotion recognition when the neural network output result is not clear, especially for long texts.

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Emoji Helps! A Multi-modal Siamese Architecture for Tweet User Verification

Cited by 18 publications

References 24 publications

BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

A multi-modal and multi-scale emotion-enhanced inference model based on fuzzy recognition

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