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ML-For-Beginners/translations/fi/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb

100 lines
3.0 KiB

{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": 3
},
"orig_nbformat": 4,
"coopTranslator": {
"original_hash": "27de2abc0235ebd22080fc8f1107454d",
"translation_date": "2025-09-04T09:29:59+00:00",
"source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
"language_code": "fi"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from textblob import TextBlob\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You should download the book text, clean it, and import it here\n",
"with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
" file_contents = f.read()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"book_pride = TextBlob(file_contents)\n",
"positive_sentiment_sentences = []\n",
"negative_sentiment_sentences = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for sentence in book_pride.sentences:\n",
" if sentence.sentiment.polarity == 1:\n",
" positive_sentiment_sentences.append(sentence)\n",
" if sentence.sentiment.polarity == -1:\n",
" negative_sentiment_sentences.append(sentence)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
"for sentence in positive_sentiment_sentences:\n",
" print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\" \", \" \")))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
"for sentence in negative_sentiment_sentences:\n",
" print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\" \", \" \")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n---\n\n**Vastuuvapauslauseke**: \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulisi pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa väärinkäsityksistä tai virhetulkinnoista, jotka johtuvat tämän käännöksen käytöstä.\n"
]
}
]
}