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

87 lines
2.1 KiB

3 years ago
{
"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
},
"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(\" \", \" \")))"
]
}
]
}