• Corpus ID: 211139927

1 Emote-Controlled Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch . tv Channels

  title={1 Emote-Controlled Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch . tv Channels},
  author={Konstantin Kobs and Albin Zehe and Armin Bernstetter and Julian Chibane and Jan and Pfister}

Design and Development of an Emoji Sentiment Lexicon

A method for effective and efficient determination of emojis’ sentiments and their compilation in a sentiment lexicon was developed and it was shown that the developed method is able to efficiently produce similar results as sentiment lexicons produced with manual annotation.

Detecting Potential Subscribers on Twitch: A Text Mining Approach with XGBoost - Discovery Challenge ChAT: CoolStoryBob

The motivation of this research is to detect potential subscribers by predicting a user’s subscription status using a trained ML model, which can then be targeted with marketing campaigns.

Towards Predicting the Subscription Status of Twitch.tv Users - ECML-PKDD ChAT Discovery Challenge 2020

It is investigated whether the subscription status of active users of Twitch can be inferred from their activity patterns in the chats of streamers, and interaction behavior plays a crucial role in solving this task.

The Design Space of Livestreaming Equipment Setups: Tradeoffs, Challenges, and Opportunities

A holistic overview of modern livestreaming equipment in 2022 is presented by analyzing 40 videos where streamers talk about various aspects of their setups and found that each streamer must make tradeoffs between lower- and higher-fidelity options within each dimension.

Measuring 9 Emotions of News Posts from 8 News Organizations across 4 Social Media Platforms for 8 Months

Using Plutchik’s wheel of emotions framework, we identify the emotional content of 133,487 social media posts and the audience’s emotional engagement expressed in 2,824,162 comments on those posts.

Higher-Order Graph Models: From Theoretical Foundations to Machine Learning (Dagstuhl Seminar 21352)

The Dagstuhl Seminar 21352 “Higher-Order Graph Models: From Theoretical Foundations to Machine Learning” aimed at the development of a common language and a shared understanding of key challenges in the field that foster progress in data analytics and machine learning for data with complex relational structure.

Emojis in Lexicon-Based Sentiment Analysis: Creating Emoji Sentiment Lexicons from Unlabeled Corpora

A method to effectively and efficiently determine the sentiments expressed by emojis without the need for manual annotation is presented and a comparison with gold standard emoji sentiment lexicons has shown that the developed method achieves qualitatively similar results as a manual annotation.

Finding epic moments in live content through deep learning on collective decisions

This study identifies enjoyable moments in user-generated live video content by examining the audiences’ collective evaluation of its epicness and presents a deep learning model to extract them based on analyzing two million user-recommended clips and the associated chat conversations.

FeelsGoodMan: Inferring Semantics of Twitch Neologisms

A simple but powerful unsupervised framework based on word embeddings andNN to enrich existing models with out-of- 018 vocabulary knowledge is produced to establish a new baseline for sentiment analysis on Twitch data, outperforming the previous benchmark 014 by 7.36 percentage points.



Modeling and Analyzing the Video Game Live-Streaming Community

A model to characterize how streamers and spectators behave, based on their possible actions in Twitch, is proposed and, using it, a case study is performed on the Star craft II streamer and spectators.

Multimedia Lab $@$ ACL WNUT NER Shared Task: Named Entity Recognition for Twitter Microposts using Distributed Word Representations

A semisupervised system that detects 10 types of named entities that achieved the fourth position in the final ranking, without using any kind of hand-crafted features such as lexical features or gazetteers.

Language-Independent Twitter Sentiment Analysis

The experiments show that the classification approach can be applied effectively for multiple languages without requiring extra effort per additional language, and the sentiment evaluation dataset publicly available.

Efficient Estimation of Word Representations in Vector Space

Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.

SemEval-2017 Task 4: Sentiment Analysis in Twitter

The fifth year of the Sentiment Analysis in Twitter task is described, with a new language, Arabic, for all subtasks, and information from the profiles of the Twitter users who posted the target tweets made available.


It is the purpose of this paper to analyse a class of distribution functions that appears in a wide range of empirical data-particularly data describing sociological, biological and economic

SemEval-2014 Task 9: Sentiment Analysis in Twitter

The Sentiment Analysis in Twitter task is described, a continuation of the last year’s task that ran successfully as part of SemEval2013 and introduced three new test sets: regular tweets, sarcastic tweets, and LiveJournal sentences.

SemEval-2016 Task 4: Sentiment Analysis in Twitter

The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams.

Night Mode, Dark Thoughts: Background Color Influences the Perceived Sentiment of Chat Messages

It is claimed that user sentiment perception can be influenced by interface color, especially for ambiguous textual content laced with irony and sarcasm, and can be applied in persuasive interaction and user experience design across the entirety of the digital landscape.