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13macattack37
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07962c4
Adding a main for testing text_classifier
Browse files- text-classifier/main.py +48 -0
text-classifier/main.py
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from text_classifier import text_classifier
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# Some examples of tweets:
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data_dict = {'25 years ago we made a promise to the people of Hong Kong. We intend to keep it. https://t.co/nIN96ZydgV': {'hour': '17',
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'nlikes': 7878,
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'nreplies': 2999,
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'nretweets': 1993,
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'topic': '',
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'sentiment': ''},
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'A huge delight to meet @SwedishPM Magdalena Andersson and President @niinisto again. The accession of Finland and Sweden to @NATO will permanently strengthen our defensive Alliance, helping to keep us all safe. #WeAreNATO https://t.co/pArvdWHr2F': {'hour': '16',
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'nlikes': 3468,
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'nreplies': 686,
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'nretweets': 435,
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'topic': '',
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'sentiment': ''},
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'At this @NATO Leaders’ Summit, I’ll be urging fellow nations to continue to do everything they can to support Ukraine. The UK has always played a historic role in the @NATO alliance, working to address the biggest global threats and build a more secure world.': {'hour': '07',
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'nlikes': 7742,
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'nreplies': 1838,
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'nretweets': 1112,
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'topic': '',
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'sentiment': ''},
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'Morgan Johansson måste avgå som minister. Otryggheten biter sig fast och gängkriminaliteten är allt annat än knäckt. Antalet skjutningar ökar och sätter skräck i varje del av vårt land. Sverige har förvandlats till ett gangsterland.': {'hour': '16',
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'nlikes': 3468,
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'nreplies': 686,
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'nretweets': 435,
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'topic': '',
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'sentiment': ''},
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'Döms man för brott, särskilt våldsbrott, ska man vara inlåst från det att domen faller tills straffet är avtjänat. Allt annat är vansinne.': {'hour': '16',
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'nlikes': 3468,
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'nreplies': 686,
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'nretweets': 435,
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'topic': '',
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'sentiment': ''},
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'Motionerna: ' + '\n' + 'K339 avslogs av enig riksdag (inkl KD).' + '\n' + 'K220 avslogs av enig riksdag (inkl KD).' + '\n' + '1601 avslogs av enig riksdag (inkl KD).' + '\n' + 'K281 avslogs av enig riksdag (inkl KD).' + '\n' + '\n' + '¯\_(ツ)_/¯': {'hour': '16',
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'nlikes': 3468,
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'nreplies': 686,
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'nretweets': 435,
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'topic': '',
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'sentiment': ''}
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}
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# Classify the TOPICS and insert the results into the data dictionary found above
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topic_results = text_classifier.classify_topics(data_dict)
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# Classify the SENTIMENTS and insert the results into the data dictionary found above
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sentiment_results = text_classifier.classify_sentiments(data_dict)
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# Print simple statistics related to TOPICS and SENTIMENTS
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text_classifier.print_stats(sentiment_results)
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