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| from TextClassifier import TextClassifier | |
| # Some examples of tweets: | |
| 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', | |
| 'nlikes': 7878, | |
| 'nreplies': 2999, | |
| 'nretweets': 1993, | |
| 'topic': '', | |
| 'sentiment': ''}, | |
| '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', | |
| 'nlikes': 3468, | |
| 'nreplies': 686, | |
| 'nretweets': 435, | |
| 'topic': '', | |
| 'sentiment': ''}, | |
| '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', | |
| 'nlikes': 7742, | |
| 'nreplies': 1838, | |
| 'nretweets': 1112, | |
| 'topic': '', | |
| 'sentiment': ''}, | |
| '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', | |
| 'nlikes': 3468, | |
| 'nreplies': 686, | |
| 'nretweets': 435, | |
| 'topic': '', | |
| 'sentiment': ''}, | |
| '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', | |
| 'nlikes': 3468, | |
| 'nreplies': 686, | |
| 'nretweets': 435, | |
| 'topic': '', | |
| 'sentiment': ''}, | |
| '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', | |
| 'nlikes': 3468, | |
| 'nreplies': 686, | |
| 'nretweets': 435, | |
| 'topic': '', | |
| 'sentiment': ''} | |
| } | |
| # Classify the TOPICS and insert the results into the data dictionary found above | |
| topic_results = TextClassifier.classify_topics(data_dict) | |
| # Classify the SENTIMENTS and insert the results into the data dictionary found above | |
| sentiment_results = TextClassifier.classify_sentiments(data_dict) | |
| # Print simple statistics related to TOPICS and SENTIMENTS | |
| TextClassifier.print_stats(sentiment_results) | |