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ML-For-Beginners/translations/hr/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": "2025-09-04T09:30:05+00:00",
"source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
"language_code": "hr"
}
},
"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**Odricanje od odgovornosti**: \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije proizašle iz korištenja ovog prijevoda.\n"
]
}
]
}