adds link to full package to readme

.gitignore

@@ -2,6 +2,8 @@
 **/*lock*
 **/*-slice*.csv
 **/*.zip
+**/*.html
+**/*.htm
 /ALL-SENATORS-LONG.csv
 /ALL-SENATORS.csv
 /collect2.py

Classification.py

@@ -1,113 +0,0 @@
-import re
-import string
-import numpy as np
-import pandas as pd
-from datetime import datetime
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-from datasets import load_dataset
-from transformers.pipelines.pt_utils import KeyDataset
-from funs.CleanTweets import remove_URL, remove_emoji, remove_html, remove_punct
-
-
-#%%
-# prepare & define paths
-# install xformers (pip install xformers) for better performance
-###################
-# Setup directories
-# WD Michael
-wd = "/home/michael/Documents/PS/Data/collectTweets/"
-# WD Server
-# wd = '/home/yunohost.multimedia/polsoc/Politics & Society/TweetCollection/'
-
-# datafile input directory
-di = "data/IN/"
-
-# Tweet-datafile output directory
-ud = "data/OUT/"
-
-# Name of file that all senator data will be written to
-senCSV = "SenatorsTweets-OnlyCov.csv"
-
-# Name of Classify datafile
-senCSVClassifiedPrep = "Tweets-Classified-Prep.csv"
-senCSVClassifiedResult = "Tweets-Classified-Results.csv"
-
-# don't change this one
-senCSVPath = wd + ud + senCSV
-senCSVcClassificationPrepPath = wd + ud + senCSVClassifiedPrep
-senCSVcClassificationResultPath = wd + ud + senCSVClassifiedResult
-
-#%%
-# get datafra,e
-dfClassify = pd.read_csv(senCSVPath, dtype=(object))
-
-# dataframe from csv
-dfClassify['fake'] = False
-
-
-#%%
-# https://huggingface.co/bvrau/covid-twitter-bert-v2-struth
-# HowTo:
-# https://huggingface.co/docs/transformers/main/en/model_doc/bert#transformers.BertForSequenceClassification
-# https://stackoverflow.com/questions/75932605/getting-the-input-text-from-transformers-pipeline
-pipe = pipeline("text-classification", model="bvrau/covid-twitter-bert-v2-struth")
-model = AutoModelForSequenceClassification.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
-tokenizer = AutoTokenizer.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
-
-# Source https://www.kaggle.com/code/daotan/tweet-analysis-with-transformers-bert
-
-dfClassify['cleanContent'] = dfClassify['rawContent'].apply(remove_URL)
-dfClassify['cleanContent'] = dfClassify['cleanContent'].apply(remove_emoji)
-dfClassify['cleanContent'] = dfClassify['cleanContent'].apply(remove_html)
-dfClassify['cleanContent'] = dfClassify['cleanContent'].apply(remove_punct)
-dfClassify['cleanContent'] = dfClassify['cleanContent'].apply(lambda x: x.lower())
-
-#%%
-# remove empty rows
-dfClassify.cleanContent.replace('',np.nan,inplace=True)
-dfClassify.dropna(subset=['cleanContent'], inplace=True)
-
-#%%
-timeStart = datetime.now() # start counting execution time
-
-max_length = 128
-dfClassify['input_ids'] = dfClassify['cleanContent'].apply(lambda x: tokenizer(x, max_length=max_length, padding="max_length",)['input_ids'])
-#train.rename(columns={'target': 'labels'}, inplace=True)
-#train.head()
-
-# %%
-dfClassify.to_csv(senCSVcClassificationPrepPath, encoding='utf-8', columns=['id', 'cleanContent'])
-
-#%%
-dataset = load_dataset("csv", data_files=senCSVcClassificationPrepPath)
-
-# %%from datetime import datetime
-
-#from tqdm.auto import tqdm
-#for out in tqdm(pipe(KeyDataset(dataset['train'], "cleanContent"))):
-#    print(out)
-
-#%% 
-output_labels = []
-output_score = []
-for out in pipe(KeyDataset(dataset['train'], "cleanContent"), batch_size=8, truncation="only_first"):
-    output_labels.append(out['label'])
-    output_score.append(out['score'])
-    # [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
-    # Exactly the same output as before, but the content are passed
-    # as batches to the model
-# %%
-dfClassify['output_label'] = output_labels
-dfClassify['output_score'] = output_score
-
-timeEnd = datetime.now()
-timeTotal = timeEnd - timeStart
-timePerTweet = timeTotal / 96
-
-print(f"Total classification execution time: {timeTotal} seconds")
-print(f"Time per tweet classification: {timePerTweet}")
-
-# %%
-dfClassify.to_csv(senCSVcClassificationResultPath, encoding='utf-8')
-
-# %%

ClassificationFake.py

@@ -1,12 +1,9 @@
-import re
-import string
 import numpy as np
 import pandas as pd
 from datetime import datetime
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 from datasets import load_dataset
 from transformers.pipelines.pt_utils import KeyDataset
-from funs.CleanTweets import remove_URL, remove_emoji, remove_html, remove_punct
 
 
 #%%
@@ -26,11 +23,11 @@ di = "data/IN/"
 ud = "data/OUT/"
 
 # Name of file that all senator data will be written to
-senCSV = "SenatorsTweets-OnlyCov.csv"
+senCSV = "Tweets-Classified-Topic-Results.csv"
 
 # Name of Classify datafile
-senCSVClassifiedPrep = "Tweets-Classified-Topic-Prep.csv"
+senCSVClassifiedPrep = "Tweets-Classified-Fake-Prep.csv"
-senCSVClassifiedResult = "Tweets-Classified-Topic-Results.csv"
+senCSVClassifiedResult = "Tweets-Classified-Fake-Results.csv"
 
 # don't change this one
 senCSVPath = wd + ud + senCSV
@@ -46,6 +43,16 @@ import CleanTweets
 #%%
 # get datafra,e
 dfClassify = pd.read_csv(senCSVPath, dtype=(object))
+def encode_labels(label):
+    if label == 'True':
+        return 'False'
+    elif label == 'False':
+        return 'True'
+    return 0
+dfClassify['output_label_topicCov'] = dfClassify['output_label_topicCov'].apply(encode_labels)
+dfClassify.to_csv("/home/michael/Documents/PS/Data/collectTweets/data/OUT/Tweets-Classified-Topic-Results.csv", encoding='utf-8')
+
+dfClassify = dfClassify[dfClassify['output_label_topicCov']=='True'] 
 
 # dataframe from csv
 dfClassify['fake'] = False
@@ -56,9 +63,9 @@ dfClassify['fake'] = False
 # HowTo:
 # https://huggingface.co/docs/transformers/main/en/model_doc/bert#transformers.BertForSequenceClassification
 # https://stackoverflow.com/questions/75932605/getting-the-input-text-from-transformers-pipeline
-pipe = pipeline("text-classification", model="bvrau/covid-twitter-bert-v2-struth")
+pipe = pipeline("text-classification", model="/home/michael/Documents/PS/Data/collectTweets/models/FakeClass/2023-08-15_14-35-43/")
-model = AutoModelForSequenceClassification.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
+model = AutoModelForSequenceClassification.from_pretrained("/home/michael/Documents/PS/Data/collectTweets/models/FakeClass/2023-08-15_14-35-43/")
-tokenizer = AutoTokenizer.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
+tokenizer = AutoTokenizer.from_pretrained("/home/michael/Documents/PS/Data/collectTweets/models/FakeClass/2023-08-15_14-35-43/")
 
 # Source https://www.kaggle.com/code/daotan/tweet-analysis-with-transformers-bert
 
@@ -100,8 +107,8 @@ for out in pipe(KeyDataset(dataset['train'], "cleanContent"), batch_size=8, trun
     # Exactly the same output as before, but the content are passed
     # as batches to the model
 # %%
-dfClassify['output_label'] = output_labels
+dfClassify['output_label_fake'] = output_labels
-dfClassify['output_score'] = output_score
+dfClassify['output_score_fake'] = output_score
 
 timeEnd = datetime.now()
 timeTotal = timeEnd - timeStart

ClassificationTopic.py

@@ -1,12 +1,9 @@
-import re
-import string
 import numpy as np
 import pandas as pd
 from datetime import datetime
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 from datasets import load_dataset
 from transformers.pipelines.pt_utils import KeyDataset
-from funs.CleanTweets import remove_URL, remove_emoji, remove_html, remove_punct
 
 
 #%%
@@ -99,8 +96,8 @@ for out in pipe(KeyDataset(dataset['train'], "cleanContent"), batch_size=8, trun
     # Exactly the same output as before, but the content are passed
     # as batches to the model
 # %%
-dfClassify['output_label'] = output_labels
+dfClassify['output_label_topicCov'] = output_labels
-dfClassify['output_score'] = output_score
+dfClassify['output_score_topicCov'] = output_score
 
 timeEnd = datetime.now()
 timeTotal = timeEnd - timeStart
@@ -113,3 +110,14 @@ print(f"Time per tweet classification: {timePerTweet}")
 dfClassify.to_csv(senCSVcClassificationResultPath, encoding='utf-8')
 
 # %%
+## corrections
+def encode_labels(label):
+    if label == 'real':
+        return 'True'
+    elif label == 'fake':
+        return 'False'
+    return 0
+dfClassify['output_label_topicCov'] = dfClassify['output_label_topicCov'].apply(encode_labels)
+dfClassify.to_csv(senCSVcClassificationResultPath, encoding='utf-8')
+#still wrong, will be corrected in ClassificationFake.py
+

README.md

@@ -1,7 +1,131 @@
-# How to use
+# Requirements
 
-Execute collect.py to scrape tweets and generate the ´ALL-SENATORS-TWEETS.csv´.
+- python 3.10+
+- snscrape 0.6.2.20230321+ (see git repo in this folder)
+- transformers 4.31.0
+- numpy 1.23.5
+- pandas 2.0.3
+- scikit-learn 1.3.0
+- torch 2.0.1
 
-Execute collectSenData.py to scrape senator data and generate ´ALL-SENATORS.csv´.
+# About
 
-All new files will be written to ´data/OUT/´. Necessary data has to be located in ´data/IN/´
+This collection of scripts scrapes tweets of US-senators in the time from 2020-01-01T00:00:00Z to 2023-01-03T00:00:00Z, scrapes account data of the senators, prepares the tweets for the training of a NLP-model, trains two models to (1) classify the tweets topic as covid or non-covid and (2) the tweets as either "fake news" tweets or "non-fake news" tweets.
+Training only works with a prepared dataset in which the tweets are pre classified.
+More info in the comments of the scripts.
+Due to time constraints, most of the code is procedurally coded and ugly but effective.
+
+# How to
+
+Tested on Ubuntu 22.04. 
+If needed, the virual environment can be exported and send to you.
+
+All files in the folder data/in have to exist in order to execute the scripts.
+Execute in the following order:
+
+01 collect.py (see more for further info on scraping)
+02 collectSenData.py
+03 cleanTweets
+04 preTestClassification.py
+05 trainTopic.py
+06 trainFake.py
+07 ClassificationFake.py
+08 ClassificationTopic.py
+
+# Files & Folders
+
+Datafiles are not included in the repository but can be found in the full package that can be downloaded from [here](https://ncloud.mischbeck.de/s/T4QcMDSfYSkadYC) (password protected).
+
+```
+├── data
+│   ├── IN
+│   │   ├── counterKeywordsFinal.txt
+│   │   ├── counterKeywords.txt
+│   │   ├── keywords-raw.txt
+│   │   ├── keywords.txt
+│   │   ├── own_keywords.txt
+│   │   ├── pretest-tweets_fake.txt				contains tweet ids for pretest
+│   │   ├── pretest-tweets_not_fake.txt			contains tweet ids for pretest
+│   │   └── senators-raw.csv					senator datafile
+│   ├── OUT
+│   │   ├── ALL-SENATORS-TWEETS.csv
+│   │   ├── graphs
+│   │   │   ├── Timeline.png
+│   │   │   ├── Wordcloud-All.png
+│   │   │   └── Wordcloud-Cov.png
+│   │   ├── Pretest-Prep.csv
+│   │   ├── Pretest-Results.csv
+│   │   ├── Pretest-SENATORS-TWEETS.csv
+│   │   ├── profiles							dataset profiles
+│   │   │   ├── AllTweets.html
+│   │   │   └── CovTweets.html
+│   │   ├── SenatorsTweets-Final.csv
+│   │   ├── SenatorsTweets-OnlyCov.csv
+│   │   ├── SenatorsTweets-train-CovClassification.csv
+│   │   ├── SenatorsTweets-train-CovClassificationTRAIN.csv
+│   │   ├── SenatorsTweets-train-CovClassification.tsv
+│   │   ├── SenatorsTweets-train-FakeClassification.csv
+│   │   ├── SenatorsTweets-train-FakeClassificationTRAIN.csv
+│   │   ├── SenatorsTweets-train-FakeClassification.tsv
+│   │   ├── SenatorsTweets-Training.csv
+│   │   ├── SenatorsTweets-Training_WORKING-COPY.csv
+│   │   ├── topClass-PRETEST-Prep.csv
+│   │   ├── topClass-PRETEST-Results.csv
+│   │   ├── Tweets-All-slices.zip
+│   │   ├── Tweets-Classified-Fake-Prep.csv
+│   │   ├── Tweets-Classified-Fake-Results.csv
+│   │   ├── Tweets-Classified-Prep.csv
+│   │   ├── Tweets-Classified-Topic-Prep.csv
+│   │   ├── Tweets-Classified-Topic-Results.csv
+│   │   └── Tweets-Stub.csv
+├── funs
+│   ├── CleanTweets.py					2023-01-03T00:00:00Z		multiple functions to clean tweet contents for NLN-processing
+│   ├── ClearDupes.py							function for deletion of duplicate keywords
+│   ├── __init__.py
+│   ├── Scrape.py								scraper functions to be used for multiprocessing
+│   └── TimeSlice.py							time slice script to slice the time span in 24 slices, speeds up scraping through multiprocessing
+├── log											logs of the scraping process
+│   ├── log_2023-06-23_21-06-10_err.log
+│   ├── log_2023-06-23_21-06-10.log
+│   └── log_2023-06-23_21-06-10_missing.log
+├── models
+│   ├── CovClass								Covid tweet classification model
+│   │   └── 2023-08-15_05-56-50
+│   │       ├── 2023-08-15_05-56-50.csv			training output
+│   │       ├── config.json
+│   │       ├── pytorch_model.bin
+│   │       ├── special_tokens_map.json
+│   │       ├── tokenizer_config.json
+│   │       ├── tokenizer.json
+│   │       └── vocab.txt
+│   └── FakeClass								Fake tweet classification model
+│       └── 2023-08-15_14-35-43
+│           ├── 2023-08-15_14-35-43.csv			training output
+│           ├── config.json
+│           ├── pytorch_model.bin
+│           ├── special_tokens_map.json
+│           ├── tokenizer_config.json
+│           ├── tokenizer.json
+│           └── vocab.txt
+├── snscrape									contains snscrape 0.6.2.20230321+ git repo
+├── ClassificationFake.py						classifies tweets as fake or non-fake, saves:
+│													Tweets-Classified-Fake-Prep.csv		- prepared training dataset
+│													Tweets-Classified-Fake-Results.csv	- Tweets-Classified-Topic-Results.csv with cov classification results
+├── ClassificationTopic.py						classifies tweet topic, saves: 
+│													Tweets-Classified-Topic-Prep.csv 	- prepared training dataset
+│													Tweets-Classified-Topic-Results.csv	- SenatorsTweets-OnlyCov.csv with cov classification results
+├── cleanTweets.py								Curates keywordlists 
+│												Merges senator and tweet datasets
+│												Creates multiple datasets:
+│													SenatorsTweets-Final.csv	- all tweets with keyword columns
+│													SenatorsTweets-OnlyCov.csv	- only covid tweets, filtered by keywordlist
+│													SenatorsTweets-Training.csv	- training dataset, containing ~1800 randomly selected tweets from SenatorsTweets-OnlyCov.csv
+├── collect.py									scrapes tweets, saves to ALL-SENATORS-TWEETS.csv
+├── collectSenData.py							scrapes senator account data, saves to ALL-SENATORS.csv
+├── createGraphs.py								creates wordcloud & timeline graphs
+├── preTestClassification.py					pretest script that uses bvrau/covid-twitter-bert-v2-struth to analyze 100 preclassified tweets
+├── profiler.py									creates dataset profiles
+├── README.md									readme
+├── trainFake.py								training script for the fake tweet classification model
+└── trainTopic.py								training script for the tweet topic classification model
+```

analyze.py

@@ -1,129 +0,0 @@
-import re
-import string
-import numpy as np
-import pandas as pd
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-from datasets import load_dataset
-from transformers.pipelines.pt_utils import KeyDataset
-from funs.CleanTweets import remove_URL, remove_emoji, remove_html, remove_punct
-
-
-#%%
-# prepare
-# install xformers (pip install xformers) for better performance
-###################
-# Setup directories
-# WD Michael
-wd = "/home/michael/Documents/PS/Data/collectTweets/"
-# WD Server
-# wd = '/home/yunohost.multimedia/polsoc/Politics & Society/TweetCollection/'
-
-# datafile input directory
-di = "data/IN/"
-
-# Tweet-datafile output directory
-ud = "data/OUT/"
-
-# Name of file that all senator data will be written to
-senCSV = "ALL-SENATORS-TWEETS.csv"
-
-# Name of new datafile generated
-senCSVc = "Tweets-Stub.csv"
-
-# Name of pretest files
-preTestIDsFake = "pretest-tweets_fake.txt"
-preTestIDsNot = "pretest-tweets_not_fake.txt"
-
-# Name of pretest datafile
-senCSVPretest = "Pretest.csv"
-senCSVPretestPrep = "Pretest-Prep.csv"
-senCSVPretestResult = "Pretest-Results.csv"
-
-
-# don't change this one
-senCSVPath = wd + ud + senCSV
-senCSVcPath = wd + ud + senCSVc
-senCSVcPretestPath = wd + ud + senCSVPretest
-senCSVcPretestPrepPath = wd + ud + senCSVPretestPrep
-senCSVcPretestResultPath = wd + ud + senCSVPretestResult
-preTestIDsFakePath = wd + di + preTestIDsFake
-preTestIDsNotPath = wd + di + preTestIDsNot
-
-# List of IDs to select
-# Read the IDs from a file
-preTestIDsFakeL = []
-preTestIDsNotL  = []
-with open(preTestIDsFakePath, "r") as file:
-    lines = file.readlines()
-    for line in lines:
-        tid = line.strip()  # Remove the newline character
-        preTestIDsFakeL.append(tid)
-with open(preTestIDsNotPath, "r") as file:
-    lines = file.readlines()
-    for line in lines:
-        tid = line.strip()  # Remove the newline character
-        preTestIDsNotL.append(tid)
-
-# Select rows based on the IDs
-df = pd.read_csv(senCSVPath, dtype=(object))
-#%%
-# Create pretest dataframe
-dfPreTest = df[df['id'].isin(preTestIDsFakeL)].copy()
-dfPreTest['fake'] = True
-dfPreTest = pd.concat([dfPreTest, df[df['id'].isin(preTestIDsNotL)]], ignore_index=True)
-dfPreTest['fake'] = dfPreTest['fake'].fillna(False)
-
-#%%
-# https://huggingface.co/bvrau/covid-twitter-bert-v2-struth
-# HowTo:
-# https://huggingface.co/docs/transformers/main/en/model_doc/bert#transformers.BertForSequenceClassification
-# https://stackoverflow.com/questions/75932605/getting-the-input-text-from-transformers-pipeline
-pipe = pipeline("text-classification", model="bvrau/covid-twitter-bert-v2-struth")
-model = AutoModelForSequenceClassification.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
-tokenizer = AutoTokenizer.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
-
-# Source https://www.kaggle.com/code/daotan/tweet-analysis-with-transformers-bert
-
-dfPreTest['cleanContent'] = dfPreTest['rawContent'].apply(remove_URL)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(remove_emoji)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(remove_html)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(remove_punct)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(lambda x: x.lower())
-
-#%%
-max_length = 128
-dfPreTest['input_ids'] = dfPreTest['cleanContent'].apply(lambda x: tokenizer(x, max_length=max_length, padding="max_length",)['input_ids'])
-#train.rename(columns={'target': 'labels'}, inplace=True)
-#train.head()
-
-# %%
-dfPreTest.to_csv(senCSVcPretestPrepPath, encoding='utf-8', columns=['id', 'cleanContent'])
-
-
-#%%
-dataset = load_dataset("csv", data_files=senCSVcPretestPrepPath)
-
-# %%
-results = pipe(KeyDataset(dataset, "text"))
-# %%
-#from tqdm.auto import tqdm
-#for out in tqdm(pipe(KeyDataset(dataset['train'], "cleanContent"))):
-#    print(out)
-
-#%% 
-output_labels = []
-output_score = []
-for out in pipe(KeyDataset(dataset['train'], "cleanContent"), batch_size=8, truncation="only_first"):
-    output_labels.append(out['label'])
-    output_score.append(out['score'])
-    # [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
-    # Exactly the same output as before, but the content are passed
-    # as batches to the model
-# %%
-dfPreTest['output_label'] = output_labels
-dfPreTest['output_score'] = output_score
-
-# %%
-dfPreTest.to_csv(senCSVcPretestResultPath, encoding='utf-8')
-
-# %%

cleanTweets.py

@@ -9,7 +9,8 @@ Created on Mon Jun 26 20:36:43 2023
 import pandas as pd
 # import pyreadstat
 import numpy as np
-from funs.ClearDupes import deDupe
+import sys
+
 
 # Seet for training dataset generation
 seed = 86431891
@@ -49,6 +50,11 @@ senDatasetPath = wd + di + senDataset
 
 df = pd.read_csv(senCSVPath, dtype=(object))
 
+## Import own functions
+funs = wd+"funs"
+sys.path.insert(1, funs)
+from ClearDupes import deDupe
+
 mixed_columns = df.columns[df.nunique() != len(df)]
 print(mixed_columns)
 

collect.py

@@ -66,7 +66,6 @@ which is the final output.
 import os
 import pandas as pd
 import glob
-import time
 import sys
 from datetime import datetime
 import concurrent.futures
@@ -149,10 +148,12 @@ tweetDFColumns = [
 ################## do NOT change anything below this line ###################
 #############################################################################
 
-## Import functions
+## Import own functions
-from funs.TimeSlice import *
+funs = wd+"funs"
-from funs.ClearDupes import deDupe
+sys.path.insert(1, funs)
-from funs.Scrape import scrapeTweets
+from TimeSlice import get_Tslices
+from ClearDupes import deDupe
+from Scrape import scrapeTweets
 
 ################### 
 # Create logfile & log all outputs

data/IN/counterKeywordsFinal.txt

@@ -0,0 +1,23 @@
+meth
+gun violence
+flu season
+vaping
+chd
+addiction
+indigenous women
+separating children
+tobacco
+e-cigarette
+muslim ban
+soleimani
+cocaine
+separating families
+muslim travel ban
+usmca trade deal
+shooting
+overdose
+separated children
+coronary heart disease
+gun-violence
+opioid
+flu-season

data/IN/keywords.txt

@@ -18,44 +18,43 @@ socialdistancing
 wear a mask
 lockdown
 covd
-Coronavirus
+coronavirus
-Koronavirus
+koronavirus
-Corona
+corona
-CDC
+cdc
-Wuhancoronavirus
+wuhancoronavirus
-Wuhanlockdown
+wuhanlockdown
-Ncov
+ncov
-Wuhan
+wuhan
-N95
+n95
-Kungflu
+kungflu
-Epidemic
+epidemic
 outbreak
-Sinophobia
+sinophobia
-China
 covid-19
 corona virus
 covid
 covid19
 sars-cov-2
-COVIDー19
+covidー19
-COVD
+covd
 pandemic
 coronapocalypse
 canceleverything
-Coronials
+coronials
-SocialDistancingNow
+socialdistancingnow
-Social Distancing
+social distancing
-SocialDistancing
+socialdistancing
 panicbuy
 panic buy
 panicbuying
 panic buying
-14DayQuarantine
+14dayquarantine
-DuringMy14DayQuarantine
+duringmy14dayquarantine
 panic shop
 panic shopping
 panicshop
-InMyQuarantineSurvivalKit
+inmyquarantinesurvivalkit
 panic-buy
 panic-shop
 coronakindness
@@ -65,7 +64,7 @@ chinesevirus
 stayhomechallenge
 stay home challenge
 sflockdown
-DontBeASpreader
+dontbeaspreader
 lockdown
 lock down
 shelteringinplace
@@ -79,13 +78,13 @@ flatten the curve
 china virus
 chinavirus
 quarentinelife
-PPEshortage
+ppeshortage
 saferathome
 stayathome
 stay at home
 stay home
 stayhome
-GetMePPE
+getmeppe
 covidiot
 epitwitter
 pandemie
@@ -93,7 +92,7 @@ wear a mask
 wearamask
 kung flu
 covididiot
-COVID__19
+covid__19
 omicron
 variant
 vaccine
@@ -139,9 +138,7 @@ work from home
 workfromhome
 working from home
 workingfromhome
-ppe
 n95
-ppe
 n95
 covidiots
 covidiots

models/CovClass/2023-08-15_01-55-11/statsTopicClassification-2023-08-15_01-53-12.csv

@@ -0,0 +1,7 @@
+epoch,Training Loss,Valid. Loss,Valid. Accur.,Training Time,Validation Time
+1,0.39025546515679493,0.40877932761593355,0.9103260869565217,0:10:21,0:00:40
+2,0.3057803610952067,0.3502063500978377,0.9103260869565217,0:10:53,0:00:43
+3,0.17910970049364833,0.27903796154904464,0.9375,0:10:30,0:00:38
+4,0.09279396105943587,0.41342766528301267,0.904891304347826,0:11:03,0:00:43
+5,0.06132459050129317,0.4468563502887264,0.9239130434782609,0:12:07,0:00:44
+6,0.04195396880810895,0.4350045176675928,0.9266304347826086,0:11:21,0:00:40

preTestClassification.py

@@ -1,13 +1,8 @@
-import re
-import string
-import numpy as np
 import pandas as pd
 from datetime import datetime
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 from datasets import load_dataset
 from transformers.pipelines.pt_utils import KeyDataset
-from funs.CleanTweets import remove_URL, remove_emoji, remove_html, remove_punct
-
 
 #%%
 # prepare
@@ -40,7 +35,6 @@ senCSVPretest = "Pretest.csv"
 senCSVPretestPrep = "Pretest-Prep.csv"
 senCSVPretestResult = "Pretest-Results.csv"
 
-
 # don't change this one
 senCSVPath = wd + ud + senCSV
 senCSVcPath = wd + ud + senCSVc
@@ -50,6 +44,11 @@ senCSVcPretestResultPath = wd + ud + senCSVPretestResult
 preTestIDsFakePath = wd + di + preTestIDsFake
 preTestIDsNotPath = wd + di + preTestIDsNot
 
+import sys
+funs = wd+"funs"
+sys.path.insert(1, funs)
+import CleanTweets
+
 # List of IDs to select
 # Read the IDs from a file
 preTestIDsFakeL = []
@@ -85,11 +84,7 @@ tokenizer = AutoTokenizer.from_pretrained("bvrau/covid-twitter-bert-v2-struth")
 
 # Source https://www.kaggle.com/code/daotan/tweet-analysis-with-transformers-bert
 
-dfPreTest['cleanContent'] = dfPreTest['rawContent'].apply(remove_URL)
+dfPreTest['cleanContent'] = dfPreTest['rawContent'].apply(CleanTweets.preprocess_text)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(remove_emoji)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(remove_html)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(remove_punct)
-dfPreTest['cleanContent'] = dfPreTest['cleanContent'].apply(lambda x: x.lower())
 
 #%%
 timeStart = datetime.now() # start counting execution time

repairmystupidity.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug 14 20:47:22 2023
+
+@author: michael
+"""
+import pandas as pd
+
+wd = "/home/michael/Documents/PS/Data/collectTweets/"
+
+# datafile input directory
+di = "data/IN/"
+
+# Tweet-datafile output directory
+ud = "data/OUT/"
+
+falsch = wd + ud + "SenatorsTweets-Training_WORKING-COPY-correct.csv"
+richtig = wd + ud + "SenatorsTweets-Training.csv"
+correct = wd + ud + "SenatorsTweets-Training_WORKING-COPY-correct2.csv"
+
+# Name of new datafile generated
+senCSVprep = "SenatorsTweets-Training_WORKING-COPY-prepared"
+
+# don't change this one
+falsch = pd.read_csv(falsch, dtype=(object), sep=";")
+richtig = pd.read_csv(richtig, dtype=(object))
+
+df = pd.merge(falsch,richtig[['tid','rawContent', 'date']],on='tid', how='left')
+df.drop(columns=['rawContent_x', 'date_x'], inplace=True)
+df.rename(columns={'tid_y':'tid', 'rawContent_y':'rawContent', 'date_y':'date'}, inplace=True)
+df = df[['tid','date','topicCovid','fake','rawContent','Unnamed: 6']]
+df.rename(columns={'Unnamed: 6':'comment'}, inplace=True)
+
+df.to_csv(correct, encoding='utf-8', sep=";")

trainFake.py

@@ -15,10 +15,8 @@ from sklearn.model_selection import train_test_split # pip install scikit-learn
 
 import pandas as pd
 
-## Follow these two guides:
+## Uses snippets from this guide:
-# best one https://mccormickml.com/2019/07/22/BERT-fine-tuning/
+# https://mccormickml.com/2019/07/22/BERT-fine-tuning/
-# https://xiangyutang2.github.io/tweet-classification/
-# https://medium.com/mlearning-ai/fine-tuning-bert-for-tweets-classification-ft-hugging-face-8afebadd5dbf
 
 ###################
 # Setup directories

trainTopic.py

@@ -15,10 +15,8 @@ from sklearn.model_selection import train_test_split # pip install scikit-learn
 
 import pandas as pd
 
-## Follow these two guides:
+## Uses snippets from this guide:
-# best one https://mccormickml.com/2019/07/22/BERT-fine-tuning/
+# https://mccormickml.com/2019/07/22/BERT-fine-tuning/
-# https://xiangyutang2.github.io/tweet-classification/
-# https://medium.com/mlearning-ai/fine-tuning-bert-for-tweets-classification-ft-hugging-face-8afebadd5dbf
 
 ###################
 # Setup directories
@@ -65,11 +63,7 @@ seed = 12355
 modCovClassPath = wd + "models/CovClass/"
 modFakeClassPath = wd + "models/FakeClass/"
 
-model_name = 'digitalepidemiologylab/covid-twitter-bert-v2' # accuracy 69
-#model_name = 'justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets' #48
-#model_name = "cardiffnlp/tweet-topic-latest-multi"
 model_name = "bvrau/covid-twitter-bert-v2-struth"
-#model_name = "cardiffnlp/roberta-base-tweet-topic-single-all"
 model_fake_name = 'bvrau/covid-twitter-bert-v2-struth' 
 
 # More models for fake detection: