Pattern-Based Emotion Classification on Social Media

Tromp, Erik; Pechenizkiy, Mykola

doi:10.1007/978-3-319-18458-6_1

Cited by 19 publications

(11 citation statements)

References 26 publications

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“…Similarly, timely detection of negative sentiment towards peers, such as anger and hostility (Gachechiladze et al 2017), might be exploited for detecting code of conduct violations (Tromp and Pechenizkiy 2015) or enhancing effective community management. For example, sentiment analysis may support GitHub users who want to be notified of heated conversation and lock them before flame wars break out.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Polarity Detection for Software Development

et al. 2017

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The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines

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Section: Discussionmentioning

confidence: 99%

Sentiment Polarity Detection for Software Development

et al. 2017

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“…We expect a revised and extended version of this manuscript describing RBEM-Emo to appear in(Tromp and Pechenizkiy, 2015) …”

mentioning

confidence: 99%

Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel

Tromp¹,

Pechenizkiy²

2014

Preprint

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We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare its performance with the current state-of-the-art techniques for emotion detection, including a recursive autoencoder. The results of the experimental study suggest that RBEM-Emo is a promising approach advancing the current state-of-the-art in emotion detection.

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“…Each emotion is represented by a different colour and five circles of different size indicating five degrees of intensity, so participants can also indicate the intensity of their emotions. Plutchik's Wheel of Emotions is also well known and has been used for example to detect emotions on social media (Tromp & Pechenizkiy, 2015). It includes 8 basic emotions that are considered key to our survival.…”

Section: Methods To Evaluate Emotionsmentioning

confidence: 99%

Beyond Happy‐or‐Not: Using Emoji to Capture Visitors’ Emotional Experience

Angeli¹,

Kelly²,

O’Neill³

2020

Curator The Museum Journal

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Museums are emotionally driven sites. People visit museums to feel and their emotions influence how the museum and its artefacts are perceived. Thus, evaluating emotional states are increasingly important for museums. However, evaluating visitors' experiences is increasingly challenging, especially with the introduction of new and emerging technology. Moreover, people's behaviour is not strictly objective and rational. While emotional states are subjective and hard to verbalize or observe, emoji are often used to express emotions on mobile and smartphone messaging applications. In this paper we investigate whether emoji can capture emotional states elicited by museum experiences, supporting traditional methods such as interviews. While other non-verbal self-report methods have been used to evaluate emotions, this is the first tool of this kind designed specifically to measure emotions elicited by museum experiences. We designed a set of 9 emoji illustrating a variety of emotional states beyond happyor-not. Then, we confirmed that participants understood our emoji's intended concept using a word association task. Finally, we used our 9 emoji to evaluate an interactive museum experience. We also run interviews and we investigated the correspondence between participants' comments and the emoji they chose. Through this study we gained a better understanding of how the emoji can be deployed to capture a range of visitors' emotional experiences. Our findings suggest that emoji can capture which emotional states participants felt beyond the happy-or-not dichotomy, but that they should be complemented with traditional methods such as interviews to understand why specific emotions were felt.

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Pattern-Based Emotion Classification on Social Media

Cited by 19 publications

References 26 publications

Sentiment Polarity Detection for Software Development

Sentiment Polarity Detection for Software Development

Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel

Beyond Happy‐or‐Not: Using Emoji to Capture Visitors’ Emotional Experience

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