feat: add dataset processor class

server/dataset_processor.py

@@ -0,0 +1,39 @@
+import pandas as pd
+
+from server.analysis.nlp import NLP
+
+class DatasetProcessor:
+    def __init__(self, df, topics):
+        self.df = self._explode_comments(df)
+        self.topics = topics
+        self.nlp = NLP(self.df, "title", "content", self.topics)
+
+    def _explode_comments(self, df) -> pd.DataFrame:
+        comments_df = df[["id", "comments"]].explode("comments")
+        comments_df = comments_df[comments_df["comments"].apply(lambda x: isinstance(x, dict))]
+        comments_df = pd.json_normalize(comments_df["comments"])
+
+        posts_df = df.drop(columns=["comments"])
+        posts_df["type"] = "post"
+        posts_df["parent_id"] = None
+
+        comments_df["type"] = "comment"
+        comments_df["parent_id"] = comments_df.get("post_id")
+
+        df = pd.concat([posts_df, comments_df])
+        df.drop(columns=["post_id"], inplace=True, errors="ignore")
+
+        return df
+    
+    def enrich(self) -> pd.DataFrame:
+        self.df['timestamp'] = pd.to_numeric(self.df['timestamp'], errors='coerce')
+        self.df['date'] = pd.to_datetime(self.df['timestamp'], unit='s').dt.date
+        self.df["dt"] = pd.to_datetime(self.df["timestamp"], unit="s", utc=True)
+        self.df["hour"] = self.df["dt"].dt.hour
+        self.df["weekday"] = self.df["dt"].dt.day_name()
+        
+        self.nlp.add_emotion_cols()
+        self.nlp.add_topic_col()
+        self.nlp.add_ner_cols()
+
+        return self.df