Update README.md

README.md

@@ -1,48 +1,141 @@
----
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': Neutral
-          '1': Opinion
-          '2': Argumentative
-          '3': Expressive
-          '4': Informational
-  splits:
-  - name: train
-    num_bytes: 10199395
-    num_examples: 49268
-  - name: test
-    num_bytes: 2185702
-    num_examples: 10558
-  - name: valid
-    num_bytes: 2185495
-    num_examples: 10557
-  download_size: 9157907
-  dataset_size: 14570592
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
-  - split: valid
-    path: data/valid-*
-label2id:
-  Neutral: 0
-  Opinion: 1
-  Argumentative: 2
-  Expressive: 3
-  Informational: 4
-id2label:
-  0: Neutral
-  1: Opinion
-  2: Argumentative
-  3: Expressive
-  4: Informational
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- social_media
+- politics
+pretty_name: Classifying Social Media Comments
+size_categories:
+- 10K<n<100K
+---
+---
+
+# **Description**
+
+This dataset was created in an attempt to understand the nature of social media commentary beyond the usual 
+'positive', 'negative', 'neutral' labels.  Below is a description of the sources, labels and methods used to 
+create the dataset
+
+## **Sources**
+
+The social media comments available in this data have been pulled from the following sources
+
+- You Tube
+- Hacker News
+- MetaFilter
+- Reddit
+- BlueSky
+
+## **Labels**
+
+**Argumentative**
+- Makes specific claims, predictions, or assertions supported by reasoning
+- Uses evidence, anecdotes, or scenarios to build a case
+- The key distinction from Opinion: there's an attempt to *persuade* or *explain why*, not just state a position
+
+**Informational**
+- Shares facts, data, links, or context relevant to the discussion
+- Low emotional affect — the comment is trying to *inform*, not convince or react
+- Includes answering another commenter's question with factual content
+- The key distinction from Argumentative: presenting information without advocating for a position
+
+**Opinion**
+- States a value judgment, stance, or take without substantial reasoning
+- "This is good/bad/wrong/overrated" — the comment *asserts* but doesn't *argue*
+- The key distinction from Argumentative: no real attempt to persuade or support the claim
+- The key distinction from Expressive: the comment is making a point, not just reacting
+
+**Expressive**
+- Emotional reactions, sarcasm, jokes, venting, exclamations
+- The comment is primarily *expressing feeling* rather than making a point
+- Includes performative agreement/disagreement ("THIS," "lol exactly," "what a joke")
+- The key distinction from Opinion: no identifiable stance being taken, just affect
+
+**Neutral**
+- Clarifying or rhetorical questions, meta-commentary, off-topic remarks
+- Comments that don't clearly fit the other four categories
+- Includes simple factual questions directed at other commenters
+
+## **Methods**
+
+**Collection**
+
+The social media comments were pulled from posts in the above sources that fit the following criteria
+
+- Search query was 'politics' or 'US Politics'
+- Data range varied from 2024 to mid-Feb of 2026 depending on the nature of the site.  For instance Reddit is
+  heavily trafficked and the daily rate limit was hit for posts pulled in just the first two weeks of Feb, while
+  Metafilter posts were pulled from as far back as 2024
+- Posts with less than 10 comments were ignored, and no more than 300 comments were pulled from any one post
+
+**Labeling**
+
+A sample of 100 comments were independently labeled by 2 of our group, then compared and revised.  The rest were 
+sent via the Batch API to 3 language models: Gemini Flash 3, Chat GPT 5.1 and Calude Haiku 4.5.  Included in the 
+prompt were 10 examples of correctly labeled samples and 10 examples of samples that had been incorrectly labeled
+with the correct label provided.  The comments that had an agreement of 2 or more models were kept with the 
+reamining comments set aside for evaluation
+
+**Processing**
+
+- Approximately 2-3k duplicate comments were removed
+- NaN's were removed
+- Emojis were converted into text using the `emoji` package
+- Text was converted to lower case
+- Remaining HTML artifacts were removed
+- URL links were replaced with a '[URL]' tag
+- Some comments contained escaped characters, these were converted back e.g. (&/quot; -> ")
+
+## **Dataset_info:**  
+
+  **Features:**  
+  
+    - text -> string  
+    - label
+
+  **Splits:**  
+  
+    - name: train   
+      - num_bytes: 10.19 Mb  
+      - num_examples: 49,268  
+    - name: test
+      - num_bytes: 2.19 Mb
+      - num_examples: 10,558
+    - name: valid
+      - num_bytes: 2.19 Mb
+      - num_examples: 10,557
+    - download_size: 9.16 Mb
+    - dataset_size: 14.57 Mb
+
+  **Configs:**  
+  
+    config_name: default  
+    data_files:  
+      - split: train  
+        path: data/train-*  
+      - split: test  
+        path: data/test-*  
+      - split: valid  
+        path: data/valid-*  
+
+  **label2id:**  
+  
+    Neutral: 0  
+    Opinion: 1  
+    Argumentative: 2  
+    Expressive: 3  
+    Informational: 4  
+
+  **id2label:**  
+  
+    0: Neutral  
+    1: Opinion  
+    2: Argumentative  
+    3: Expressive  
+    4: Informational  
+
+    
+---