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

100 lines
2.9 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": "2025-10-11T12:14:54+00:00",
"source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
"language_code": "et"
}
},
"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**Lahtiütlus**: \nSee dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame tagada täpsust, palume arvestada, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Algne dokument selle algses keeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitame kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti tõlgenduste eest.\n"
]
}
]
}