DOI: 10.7910/dvn/mlrtxd
title: An Annotated Corpus of Dota 2 In-Game Chat, Labeled for Emotional States of Anxiety and Boredom

description: &lt;h1&gt;An Annotated Corpus of Dota 2 In-Game Chat, Labeled for Emotional States of Anxiety and Boredom&lt;/h1&gt;
&lt;hr&gt;


&lt;h2&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/h2&gt;
&lt;p&gt;
 This dataset provides a valuable corpus of 8,000 in-game chat messages from the popular Multiplayer Online Battle Arena (MOBA) game, &lt;em&gt;Dota 2&lt;/em&gt;. The messages have been manually annotated to classify the emotional state of the player, specifically focusing on expressions of &lt;strong&gt;anxiety&lt;/strong&gt; and &lt;strong&gt;boredom&lt;/strong&gt;. Given the high-stress, competitive, and team-dependent nature of Dota 2, player communication is a rich source of authentic, spontaneous emotional expression. This dataset is designed to support research in natural language processing (NLP), computational linguistics, psychology, and human-computer interaction (HCI) for tasks related to emotion detection, sentiment analysis, and the study of player behavior in digital environments.
&lt;/p&gt;
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&lt;h2&gt;&lt;strong&gt;Description of Data&lt;/strong&gt;&lt;/h2&gt;
&lt;p&gt;
 The data consists of textual messages exchanged between players during live Dota 2 matches. Dota 2 is a complex, real-time strategy game where two teams of five players compete to destroy the opposing team's base. Matches are often long (30-60 minutes on average) and require intense teamwork, strategic thinking, and mechanical skill. This high-stakes environment frequently elicits strong emotional responses from players, which are often articulated in the in-game chat.
&lt;/p&gt;
&lt;p&gt;
 The language captured in this dataset is informal and characteristic of online gaming communities, including the use of game-specific jargon, slang, abbreviations, typos, and emotionally charged statements. The corpus serves as a real-world example of communication under pressure.
&lt;/p&gt;
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&lt;h2&gt;&lt;strong&gt;Methodology&lt;/strong&gt;&lt;/h2&gt;
&lt;p&gt;
 The dataset was collected from publicly available Dota 2 match data. Following collection, the corpus was manually annotated by trained annotators to categorize each message based on the dominant emotional theme it conveyed.
&lt;/p&gt;
&lt;p&gt;
 Each message was assigned one of two primary labels:
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&lt;ol&gt;
 &lt;li&gt;
 &lt;strong&gt;Anxiety:&lt;/strong&gt; This label was applied to messages reflecting frustration, anger, stress, helplessness, blaming teammates, or a sense of being overwhelmed. These messages often pertain to negative game events, perceived mistakes (one's own or others'), or the pressure of a difficult match.&lt;br&gt;
 &lt;em&gt;Examples: "no chance to win with braindead afk lc", "I HATE MY TEAM SO BAD", "my hands are shaking"&lt;/em&gt;
 &lt;/li&gt;
 &lt;li&gt;
 &lt;strong&gt;Boredom:&lt;/strong&gt; This label was applied to messages indicating disengagement, a lack of challenge, or the feeling that the game is pointless or already decided. These messages often include taunts about the opponent's lack of skill or a desire for the match to end quickly due to its uncompetitive nature.&lt;br&gt;
 &lt;em&gt;Examples: "no wonder it was so easy", "this game is fucking easy", "just finish this crap"&lt;/em&gt;
 &lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;
 The annotation process focused on the contextual meaning of the message within a competitive gaming framework.
&lt;/p&gt;
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&lt;h2&gt;&lt;strong&gt;File Description&lt;/strong&gt;&lt;/h2&gt;
&lt;p&gt;
 The data is provided in a single CSV (Comma-Separated Values) file named &lt;code&gt;dota2_chat_emotion_dataset.csv&lt;/code&gt;. The file contains the following columns:
&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;code&gt;chat_message&lt;/code&gt;: The raw text of the chat message.&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;class&lt;/code&gt;: The annotated label for the message ('anxiety' (1) or 'boredom' (0)).&lt;/li&gt;
&lt;/ul&gt;
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&lt;h2&gt;&lt;strong&gt;Potential Use Cases&lt;/strong&gt;&lt;/h2&gt;
&lt;p&gt;
 This dataset is suitable for a wide range of research purposes, including but not limited to:
&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;
 &lt;strong&gt;Natural Language Processing:&lt;/strong&gt; Training, testing, and validating models for emotion detection, sentiment analysis, and toxicity classification in niche, high-jargon domains.
 &lt;/li&gt;
 &lt;li&gt;
 &lt;strong&gt;Psychology & Cognitive Science:&lt;/strong&gt; Studying emotional regulation, stress responses, and group dynamics in online and competitive settings.
 &lt;/li&gt;
 &lt;li&gt;
 &lt;strong&gt;Human-Computer Interaction (HCI) & Game Studies:&lt;/strong&gt; Investigating player experience (PX), designing systems to mitigate negative player interactions, and understanding communication patterns within online communities.
 &lt;/li&gt;
 &lt;li&gt;
 &lt;strong&gt;Sociolinguistics:&lt;/strong&gt; Analyzing the use of slang, jargon, and community-specific language under different emotional conditions.
 &lt;/li&gt;
&lt;/ul&gt;
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