1 files changed, 0 insertions, 78 deletions
diff --git a/karpet/core.py b/karpet/core.py
index 256942d..afae107 100644
--- a/karpet/core.py
+++ b/karpet/core.py
@@ -1,6 +1,5 @@
 try:
     from pytrends.request import TrendReq
-    from twitterscraper import query_tweets
 except:
     pass
 
@@ -273,83 +272,6 @@ class Karpet:
 
         return df.sort_values("date").reset_index(drop=True)
 
-    # def fetch_tweets(self, kw_list, lang, limit=None):
-    #     """
-    #     Scrapes Twitter without any limits and  returns dataframe with the
-    #     following structure
-
-    #     * fullname
-    #     * id
-    #     * likes
-    #     * replies
-    #     * retweets
-    #     * text
-    #     * timestamp
-    #     * url
-    #     * user
-    #     * date
-    #     * has_link
-
-    #     :param list kw_list: List of keywords to search for. Will be joined with "OR" operator.
-    #     :param str lang: Language of tweets to search in.
-    #     :param int limit: Limit search results. Might get really big and slow so this should help.
-    #     :return: Pandas dataframe with all search results (tweets).
-    #     :rtype: pd.DataFrame
-    #     """
-
-    #     def process_tweets(tweets):
-    #         """
-    #         Cleans up tweets and returns dataframe with the
-    #         following structure
-
-    #         * fullname
-    #         * id
-    #         * likes
-    #         * replies
-    #         * retweets
-    #         * text
-    #         * timestamp
-    #         * url
-    #         * user
-    #         * date
-    #         * has_link
-
-    #         :param list tweets: List of dicts of tweets data.
-    #         :return: Returns dataframe with all the scraped tweets (no index).
-    #         :rtype: pd.DataFrame
-    #         """
-
-    #         # 1. Clean up.
-    #         data = []
-
-    #         for t in tweets:
-    #             d = t.__dict__
-    #             del d["html"]
-    #             data.append(d)
-
-    #         # 2. Create dataframe
-    #         df = pd.DataFrame(data)
-    #         # import pdb
-
-    #         # pdb.set_trace()
-    #         df["date"] = df["timestamp"].dt.date
-    #         df["has_link"] = df["text"].apply(
-    #             lambda text: "http://" in text or "https://" in text
-    #         )
-
-    #         return df
-
-    #     try:
-    #         _ = query_tweets
-    #     except NameError:
-    #         raise Exception("Twitter extension is not installed - see README file.")
-
-    #     tweets = query_tweets(
-    #         query=" OR ".join(kw_list), begindate=self.start, lang=lang, limit=limit
-    #     )
-
-    #     return process_tweets(tweets)
-
     def fetch_news(self, symbol, limit=10):
         """
         Fetches news of the given symbol. Each news contains