Sentiment Analysis of Tweets Including Emoji Data

LeCompte, Travis; Chen, Jianhua

doi:10.1109/csci.2017.137

Cited by 29 publications

(9 citation statements)

References 8 publications

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“…As emoji are widely used in expressing emotions, they have become an effective means of sentiment analysis (Hogenboom et al, 2013; Cappallo et al, 2015). A number of studies have confirmed the effective performance of emoji in sentiment analysis (Sari et al, 2014; Cahyaningtyas et al, 2017; Felbo et al, 2017; LeCompte and Chen, 2017). Besides, emoji-based sentiment analysis is language-independent and exhibits cross-language validity (Guthier et al, 2017), for example, Al-Azani et al (2018) found that emoji can also be used in analyzing the sentiment of Arabic tweets.…”

Section: Research Fields Regarding Emojimentioning

confidence: 94%

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: 94%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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“…LeCompte and Chen 94 analyzed the effects of emoji inclusion in detecting emotions from texts. Their work adopted the Keyword Identification method and the SVM and Multinomial Naïve Bayes (MNB) to classify emotions into sad, angry, scared, happy, surprised, thankful, and love.…”

Section: Current State-of-the-art Text-based Proposalsmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

236

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Unlike common texts, due to the 280-character limit of tweets and brevity of tweet writing style, users tend to add different types of non-text information when sending tweets that can be considered as “noise,” such as emojis, mentions (i.e., mentioning other Twitter user handles), hashtags symbol (#), and URLs. Although there are many mature models for non-textual data recognition, such as emoji recognition (LeCompte and Chen, 2017 ), these non-text pattern recognition models were not considered in this study as our focus was on the detection of vaping-related text within tweets.…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification

Ren

Singh

et al. 2022

Front. Big Data

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There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes (i.e., vaping products), has dramatically increased. However, public attitudes toward vaping vary widely, and the health effects of vaping are still largely unknown. As a popular social media, Twitter contains rich information shared by users about their behaviors and experiences, including opinions on vaping. It is very challenging to identify vaping-related tweets to source useful information manually. In the current study, we proposed to develop a detection model to accurately identify vaping-related tweets using machine learning and deep learning methods. Specifically, we applied seven popular machine learning and deep learning algorithms, including Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, Multilayer Perception, Transformer Neural Network, and stacking and voting ensemble models to build our customized classification model. We extracted a set of sample tweets during an outbreak of e-cigarette or vaping-related lung injury (EVALI) in 2019 and created an annotated corpus to train and evaluate these models. After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97. All models could achieve 0.90 or higher after tuning hyperparameters. The ensemble learning model has the best average performance. Our study findings provide informative guidelines and practical implications for the automated detection of themed social media data for public opinions and health surveillance purposes.

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Sentiment Analysis of Tweets Including Emoji Data

Cited by 29 publications

References 8 publications

A Systematic Review of Emoji: Current Research and Future Perspectives

A Systematic Review of Emoji: Current Research and Future Perspectives

Text‐based emotion detection: Advances, challenges, and opportunities

Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification

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