Use the sentiment aggregation value if present

core/db/elastic.py

@@ -602,24 +602,28 @@ class ElasticsearchBackend(StorageBackend):
         if isinstance(sentiment_r, dict):
             return sentiment_r
         if sentiment_r:
+            if rule_object is not None:
+                sentiment_index = "meta.aggs.avg_sentiment.value"
+            else:
+                sentiment_index = "sentiment"
             sentiment_method, sentiment = sentiment_r
-            range_query_compare = {"range": {"sentiment": {}}}
+            range_query_compare = {"range": {sentiment_index: {}}}
             range_query_precise = {
                 "match": {
-                    "sentiment": None,
+                    sentiment_index: None,
                 }
             }
             if sentiment_method == "below":
-                range_query_compare["range"]["sentiment"]["lt"] = sentiment
+                range_query_compare["range"][sentiment_index]["lt"] = sentiment
                 add_top.append(range_query_compare)
             elif sentiment_method == "above":
-                range_query_compare["range"]["sentiment"]["gt"] = sentiment
+                range_query_compare["range"][sentiment_index]["gt"] = sentiment
                 add_top.append(range_query_compare)
             elif sentiment_method == "exact":
-                range_query_precise["match"]["sentiment"] = sentiment
+                range_query_precise["match"][sentiment_index] = sentiment
                 add_top.append(range_query_precise)
             elif sentiment_method == "nonzero":
-                range_query_precise["match"]["sentiment"] = 0
+                range_query_precise["match"][sentiment_index] = 0
                 add_top_negative.append(range_query_precise)
 
         # Add in the additional information we already populated

core/views/ui/drilldown.py

@@ -81,15 +81,21 @@ def make_graph(results):
     graph = []
     for index, item in enumerate(results):
         date = str(index)
+        sentiment = None
+        if "meta" in item:
+            if "aggs" in item["meta"]:
+                if "avg_sentiment" in item["meta"]["aggs"]:
+                    sentiment = item["meta"]["aggs"]["avg_sentiment"]["value"]
+        else:
+            if "sentiment" in item:
+                sentiment = item["sentiment"]
         graph.append(
             {
-                "text": item.get("words_noun", None)
-                or item.get("msg", None)
-                or item.get("id"),
+                "text": item.get("msg", None) or item.get("id"),
                 "nick": item.get("nick", None),
                 "channel": item.get("channel", None),
                 "net": item.get("net", None),
-                "value": item.get("sentiment", None) or None,
+                "value": sentiment,
                 "date": date,
             }
         )