intermediatetext
Sarcasm Detection
Identify sarcastic statements and label their type and target in social media and conversational text.
📝
text annotation
Configuration Fileconfig.yaml
# Sarcasm Detection Configuration
# Identify and categorize sarcastic statements
annotation_task_name: "Sarcasm Detection"
data_files:
- "data/tweets.json"
item_properties:
id_key: "id"
text_display_key: "text"
user_config:
allow_all_users: true
annotation_schemes:
- annotation_type: "radio"
name: "is_sarcastic"
description: "Is this text sarcastic?"
labels:
- name: "Sarcastic"
tooltip: "The text means the opposite of what it literally says"
key_value: "s"
- name: "Not Sarcastic"
tooltip: "The text is meant literally"
key_value: "n"
- name: "Unclear"
tooltip: "Cannot determine without more context"
key_value: "u"
- annotation_type: "radio"
name: "sarcasm_type"
description: "What type of sarcasm?"
labels:
- name: "Verbal Irony"
tooltip: "Saying the opposite of what is meant"
- name: "Self-deprecating"
tooltip: "Mocking oneself"
- name: "Situational"
tooltip: "Pointing out ironic situations"
- name: "Exaggeration"
tooltip: "Over-the-top statements"
- name: "Understatement"
tooltip: "Downplaying something significant"
show_if:
field: "is_sarcastic"
value: "Sarcastic"
- annotation_type: "radio"
name: "sarcasm_target"
description: "What is the target of the sarcasm?"
labels:
- name: "Self"
- name: "Other person"
- name: "Organization/Company"
- name: "Situation/Event"
- name: "General/Unclear"
show_if:
field: "is_sarcastic"
value: "Sarcastic"
- annotation_type: "likert"
name: "confidence"
description: "How confident are you?"
size: 5
min_label: "Not confident"
max_label: "Very confident"
- annotation_type: "text"
name: "literal_interpretation"
description: "What would this mean if taken literally?"
required: false
output: "annotation_output/"
Sample Datasample-data.json
[
{
"id": "sarc_001",
"text": "Oh great, another Monday. Just what I needed.",
"source": "twitter"
},
{
"id": "sarc_002",
"text": "I love waking up to construction noise at 6am. Really helps me start the day right.",
"source": "twitter"
}
]
// ... and 3 more itemsGet This Design
View on GitHub
Clone or download from the repository
Quick start:
git clone https://github.com/davidjurgens/potato-showcase.git cd potato-showcase/sarcasm-detection potato start config.yaml
Details
Annotation Types
radiotext
Domain
nlpsocial-media
Use Cases
sentiment-analysisirony-detection
Tags
sarcasmironysentimentsocial-medianlp
Found an issue or want to improve this design?
Open an IssueRelated Designs
Fact Verification
Verify claims as supported, refuted, or not enough information based on provided evidence.
radiotext
Reading Comprehension QA
Evaluate question-answer pairs for reading comprehension by verifying answers and rating quality.
radiospan
Commonsense Inference (ATOMIC 2020)
Annotate commonsense inferences about events, mental states, and social interactions. Based on ATOMIC 2020 (Hwang et al., AAAI 2021). Generate if-then knowledge about causes, effects, intents, and reactions.
radiotext