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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentiment analysis of Guardian World News articles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get articles from a website"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install rss parser dependency"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install feedparser"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Parsing RSS feed for world news"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import feedparser\n",
"feed_url = \"https://www.theguardian.com/world/rss\"\n",
"feed = feedparser.parse(feed_url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"for item in feed.entries:\n",
" # sanitize html\n",
" item.description = re.sub('<[^<]+?>', '', item.description)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install Vader Sentiment library and perform sentiment analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install vaderSentiment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
"analyser = SentimentIntensityAnalyzer()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sentiment_results = []\n",
"for item in feed.entries:\n",
" sentiment_title = analyser.polarity_scores(item.title)\n",
" sentiment_description = analyser.polarity_scores(item.description)\n",
" sentiment_results.append([sentiment_title['compound'], sentiment_description['compound']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install Matplotlib and visualize compound score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install matplotlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (15, 3)\n",
"plt.plot(sentiment_results, drawstyle='steps')\n",
"plt.title('Sentiment analysis relationship between title and description (Guardian World News)')\n",
"plt.legend(['title', 'description'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
