update readme

README.md

@@ -68,14 +68,14 @@ Each instance in the dataset is a pair of original-answer messages, annotated wi
 {
 "id_original": "1413960970066710533", 
 "id_answer": "1413968453690658816", 
-"original_text": "", 
-"answer_text": "", 
-"topic": "vaccines", 
-"dynamic_stance": "Disagree", 
-"original_stance": "FAVOUR", 
-"answer_stance": "AGAINST", 
-"original_emotion": ["distrust", "joy", "disgust"], 
-"answer_emotion": ["distrust"]
+  "original_text": "", 
+  "answer_text": "", 
+    "topic": "vaccines", 
+    "dynamic_stance": "Disagree", 
+  "original_stance": "FAVOUR", 
+  "answer_stance": "AGAINST", 
+  "original_emotion": ["distrust", "joy", "disgust"], 
+  "answer_emotion": ["distrust"]
 }
 ```
 
@@ -95,15 +95,15 @@ The data was collected using the Twitter API by the Barcelona Supercomputing Cen
 
 #### Initial Data Collection and Normalization
 
-The data was collected based on a list of keywords related to the five topics included in the dataset: vaccines, rent regulation, surrogate pregnancy, airport expansion, and a TV show rigging. 
+The data was collected based on a list of keywords related to the five topics included in the dataset: vaccines, rent regulation, surrogate pregnancy, airport expansion, and a TV show rigging. Specific periods in which the topic was under discussion were also selected.
 
 #### Who are the source language producers?
 
-The source language producers are user of Twitter.
+The source language producers are users of Twitter.
 
 ### Annotations
 
-- Emotions are annotated in a multi-label fashion. The labels can be: Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Distrust.
+- Emotions are annotated in a multi-label fashion. The labels can be: Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Distrust, and No emotion.
 
 - Static stance is annotated per message. The labels can be: FAVOUR, AGAINST, NEUTRAL, NA.