From eeba1bcbdd94f8238e91170a2b780974dc1e95d7 Mon Sep 17 00:00:00 2001 From: arniebarniejr Date: Wed, 1 Jul 2020 15:45:47 +0000 Subject: [PATCH] My twitter challenge codes --- twitter_mining/twitter_mining.ipynb | 82 +++++++++++++---------------- 1 file changed, 38 insertions(+), 44 deletions(-) diff --git a/twitter_mining/twitter_mining.ipynb b/twitter_mining/twitter_mining.ipynb index a6460ee..75a145f 100644 --- a/twitter_mining/twitter_mining.ipynb +++ b/twitter_mining/twitter_mining.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", @@ -61,12 +61,14 @@ "import os\n", "import json\n", "import pandas as pd\n", + "import csv\n", + "import re\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", @@ -79,6 +81,7 @@ "#install tweepy if you don't have it\n", "#!pip install tweepy\n", "import tweepy\n", + "from tweepy import API \n", "from tweepy.streaming import StreamListener\n", "from tweepy import OAuthHandler\n", "from tweepy import Stream\n", @@ -109,15 +112,12 @@ "2. If you find error, fix it. Ask for help in the slack channel if you find serious mistake\n", "3. Extend the code such that it will be useful for topics you choose to analyse\n", "4. Make nice plots and share your finding (e.g. insight on the main covid19 twitter converstions about your country)\n", - "5. Submit what ever you managed to do by Wednesday morning. But you should keep using what you build to write blogs, share on facebook, etc. \n", - "\n", - "\n", - "[Reference used to build some of the functions here](https://towardsdatascience.com/extracting-twitter-data-pre-processing-and-sentiment-analysis-using-python-3-0-7192bd8b47cf)" + "5. Submit what ever you managed to do by Wednesday morning. But you should keep using what you build to write blogs, share on facebook, etc. " ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -125,8 +125,7 @@ " '''\n", " This is a basic class to search and download twitter data.\n", " You can build up on it to extend the functionalities for more \n", - " sophisticated analysis.\n", - " \n", + " sophisticated analysis\n", " '''\n", " def __init__(self,cols=None,auth=None):\n", " #\n", @@ -147,21 +146,17 @@ " access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\n", "\n", "\n", - " #This handles Twitter authetification and the connection to Twitter \n", - " #Streaming API\n", + " #This handles Twitter authetification and the connection to Twitter Streaming API\n", " auth = OAuthHandler(consumer_key, consumer_secret)\n", " auth.set_access_token(access_token, access_token_secret)\n", " \n", - "\n", - " # \n", - " self.auth = auth\n", - " self.api = tweepy.API(auth) \n", + " api = tweepy.API(auth) \n", " \n", "\n", - " def clean_tweets(self,twitter_text):\n", + " def clean_tweets(twitter_text):\n", "\n", " #use pre processor\n", - " tweet = p.clean(twitter_text)\n", + " tweet = ppr.clean(twitter_text)\n", "\n", " #HappyEmoticons\n", " emoticons_happy = set([\n", @@ -273,13 +268,11 @@ "\n", " new_entry.append(is_sensitive)\n", "\n", - " hashtags = \", \".join([hashtag_item['text'] for \\\n", - " hashtag_item in status['entities']['hashtags']])\n", + " hashtags = \", \".join([hashtag_item['text'] for hashtag_item in status['entities']['hashtags']])\n", " new_entry.append(hashtags) #append the hashtags\n", "\n", " #\n", - " mentions = \", \".join([mention['screen_name'] for \\\n", - " mention in status['entities']['user_mentions']])\n", + " mentions = \", \".join([mention['screen_name'] for mention in status['entities']['user_mentions']])\n", " new_entry.append(mentions) #append the user mentions\n", "\n", " try:\n", @@ -305,8 +298,7 @@ " #save it to file\n", " df.to_csv(csvfile, columns=self.cols, index=False, encoding=\"utf-8\")\n", " \n", - " return df\n", - "\n" + " return df\n" ] }, { @@ -322,8 +314,8 @@ "metadata": {}, "outputs": [], "source": [ - "covid_keywords = '#COVID19 OR #COVID19Africa' #hashtag based search\n", - "tweets_file = 'data/ethiopia_covid19_23june2020.json'\n", + "covid_keywords = '#COVID19Ethiopia OR #COVID19Africa' #hashtag based search\n", + "tweets_file = 'C:\\\\users\\\\bingl\\\\Documents\\\\data_tweets.json'\n", "\n", "#get data on keywords\n", "ts = tweetsearch()\n", @@ -342,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 16, "metadata": { "colab": {}, "colab_type": "code", @@ -374,7 +366,7 @@ " print (status)\n", "\n", "def stream_tweet_data(filename='data/tweets.json',\n", - " keywords=['COVID19Africa','COVID19Ethiopia'],\n", + " keywords=['COVID19Africa','COVID19Ethiopia']\n", " is_async=False):\n", " # tweet topics to use as a filter. The tweets downloaded\n", " # will have one of the topics in their text or hashtag \n", @@ -418,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 17, "metadata": { "colab": {}, "colab_type": "code", @@ -430,8 +422,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "saving data to file: data/covid19_23june2020.json\n", - "TweetKeywords are: ['covid19', '#COVID19Africa']\n", + "saving data to file: C:\\users\\bingl\\Desktop\\data_tweets.json\n", + "TweetKeywords are: ['covid19']\n", "For testing case, please interupt the downloading process using ctrl+x after about 5 mins \n", "To keep streaming in the background, pass is_async=True\n", "Max number of tweets reached: #tweets = 1000\n" @@ -439,8 +431,8 @@ } ], "source": [ - "tweets_file = 'data/covid19_23june2020.json'\n", - "stream_tweet_data(filename=tweets_file,keywords=['covid19','#COVID19Africa']) #\n" + "tweets_file = 'C:\\\\users\\\\bingl\\\\Desktop\\\\data_tweets.json'\n", + "stream_tweet_data(filename=tweets_file,keywords=['covid19']) #\n" ] }, { @@ -453,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": { "colab": {}, "colab_type": "code", @@ -465,7 +457,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "saved numbers of tweets: 998\n" + "saved numbers of tweets: 1000\n" ] } ], @@ -485,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "metadata": { "colab": {}, "colab_type": "code", @@ -506,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 20, "metadata": { "colab": {}, "colab_type": "code", @@ -517,16 +509,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -595,7 +587,9 @@ "id": "p0VUwYR8rNLk" }, "outputs": [], - "source": [] + "source": [ + "hash_tag_list" + ] } ], "metadata": { @@ -606,9 +600,9 @@ }, "hide_input": false, "kernelspec": { - "display_name": "Python [conda env:.conda-10x]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-.conda-10x-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -620,7 +614,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" }, "latex_envs": { "bibliofile": "biblio.bib",