mirror of https://github.com/balkian/gists
Add 'repos/0d56ab9b79bfa7f2ab06/' from commit '6b2d0f3a6eeac2db41f3b3fb8596cc6598b3ae2e'
git-subtree-dir: repos/0d56ab9b79bfa7f2ab06 git-subtree-mainline:master38e6382769git-subtree-split:6b2d0f3a6e
J. Fernando Sánchez
3 years ago
commit
092e91e6a3
@ -0,0 +1,596 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Download DepecheMood from GitHub"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"ename": "SyntaxError",
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"evalue": "invalid syntax (<ipython-input-15-537542d5c5d8>, line 1)",
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"output_type": "error",
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"traceback": [
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"\u001b[1;36m File \u001b[1;32m\"<ipython-input-15-537542d5c5d8>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m wget https://github.com/marcoguerini/DepecheMood/releases/download/v1.0/DepecheMood_V1.0.zip -O latest-DepecheMood.zip\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
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]
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}
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],
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"source": [
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"!wget https://github.com/marcoguerini/DepecheMood/releases/download/v1.0/DepecheMood_V1.0.zip -O latest-DepecheMood.zip\n",
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"!yes 2>/dev/null | unzip latest-DepecheMood.zip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 283,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DepecheMood_freq.txt DepecheMood_normfreq.txt DepecheMood_tfidf.tsv DepecheMood_tfidf.txt README.txt\r\n"
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]
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}
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],
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"source": [
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"ls DepecheMood_V1.0/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 150,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"This package contains the three versions of the Lexicon 'DepecheMood' as described in the paper: \r\n",
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"\r\n",
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"Staiano, J., & Guerini, M. (2014). \"DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News\". Proceedings of ACL-2014. \r\n",
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"\r\n",
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"Each version of DepecheMood is built starting from word-by-document matrices either using raw frequencies (DepecheMood_freq.txt), normalized frequencies (DepecheMood_normfreq.txt) or tf-idf (DepecheMood_tfidf.txt). \r\n",
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"\r\n",
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"The files are tab-separated; each row contains one Lemma#PoS followed by the scores for the following emotions: AFRAID, AMUSED, ANGRY, ANNOYED, DONT_CARE, HAPPY, INSPIRED, SAD.\r\n",
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"\r\n",
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"This resource is freely available for research purposes. \r\n",
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"\r\n"
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]
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}
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],
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"source": [
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"cat DepecheMood_V1.0/README.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Lemma#PoS\r\n",
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"50#n\r\n",
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"500#n\r\n",
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"50th#a\r\n",
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"55th#a\r\n",
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"5th#a\r\n",
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"6#n\r\n",
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"60#n\r\n",
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"60th#a\r\n",
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"64th#a\r\n",
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"65th#a\r\n",
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"6th#a\r\n",
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"7#n\r\n",
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"70#n\r\n",
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"70th#a\r\n",
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"75th#a\r\n",
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"78#n\r\n",
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"7th#a\r\n",
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"8#n\r\n",
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"80#n\r\n",
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"80th#a\r\n",
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"85th#a\r\n"
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]
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}
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],
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"source": [
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"!sed -n '1p;100,120p' DepecheMood_V1.0/DepecheMood_tfidf.txt | cut -f 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
|
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"import nltk\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 398,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"lexicon = pd.DataFrame.from_csv(\"DepecheMood_V1.0/DepecheMood_normfreq.txt\", sep=\"\\t\", index_col=False)\n",
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"s = lexicon[\"Lemma#PoS\"].str.split(\"#\")\n",
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"lexicon[\"Lemma\"] = s.str[0]\n",
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"lexicon[\"PoS\"] = s.str[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 399,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 399,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"s=lexicon.index.to_series()\n",
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"s.str.contains(\"#\", False).count() # All words contain # (POS tag)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 400,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['n', 'a', 'r', 'v'], dtype=object)"
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]
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},
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"execution_count": 400,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lexicon[\"PoS\"].unique() # According to the paper, there's verbs (v), nouns (n), adjectives (a) and adverbs (r)"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 435,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#lexicon[lexicon.groupby(\"Lemma\")[\"PoS\"].transform(len) > 1] # Getting lemmas with different PoS"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 436,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"lexdict = {}\n",
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"header = []\n",
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"with open(\"DepecheMood_V1.0/DepecheMood_tfidf.tsv\") as f:\n",
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" header = f.next().strip().split()\n",
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" for l in f:\n",
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" temp = l.strip().split(\"\\t\")\n",
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" lexdict[temp[0]] = map(float, temp[1:])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Compare performance"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Pandas is great for analysis, but for sentiment analysis we need something that is quick at finding entries in the lexicon and calculating the values of each emotion. Hence, we need to compare pandas with a plain dictionary for this task."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 438,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import random\n",
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"keys = lexdict.keys()\n",
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"random.shuffle(keys)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 439,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10 loops, best of 3: 16.9 ms per loop\n",
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"10 loops, best of 3: 35.9 ms per loop\n"
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]
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}
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],
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"source": [
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"def search_all(d):\n",
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" for i in keys:\n",
|
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" if i in d:\n",
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" ja = d[i][0] + d[i][1]\n",
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" \n",
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"%timeit search_all(lexdict)\n",
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"%timeit search_all(lexicon)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 440,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"10 loops, best of 3: 34.6 ms per loop\n",
|
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"10 loops, best of 3: 39 ms per loop\n"
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]
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}
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],
|
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"source": [
|
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"def search_all(d):\n",
|
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" for i in keys:\n",
|
||||
" if i in d:\n",
|
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" ja = 0\n",
|
||||
" for j in d[i]:\n",
|
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" ja += j\n",
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" \n",
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"%timeit search_all(lexdict)\n",
|
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"%timeit search_all(lexicon.ix[:, lexicon.columns - [\"Lemma#PoS\", \"Lemma\", \"PoS\"]])"
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]
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},
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{
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"cell_type": "markdown",
|
||||
"metadata": {},
|
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"source": [
|
||||
"Apparently, it is slightly better to use a python dictionary. Plus, we avoid dependencies."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"# Sentiment Analysis"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 441,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"mapping = {\n",
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" \n",
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"#CC - Coordinating conjunction\n",
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"\"CD\": \"n\", # - Cardinal number\n",
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"#DT - Determiner\n",
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"#EX - Existential there\n",
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"\"FW\": \"n\", # - Foreign word\n",
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"#IN #- Preposition or subordinating conjunction\n",
|
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"\"JJ\": \"a\", #- Adjective\n",
|
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"\"JJR\": \"a\", # - Adjective, comparative\n",
|
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"\"JJS\": \"a\", # - Adjective, superlative\n",
|
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"#LS - List item marker\n",
|
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"#MD - Modal\n",
|
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"\"NN\": \"n\", # - Noun, singular or mass\n",
|
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"\"NNS\": \"n\", # - Noun, plural\n",
|
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"\"NNP\": \"n\", # - Proper noun, singular\n",
|
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"\"NNPS\": \"n\", # - Proper noun, plural\n",
|
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"#PDT - Predeterminer\n",
|
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"#POS - Possessive ending\n",
|
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"\"PRP\": \"n\", # - Personal pronoun\n",
|
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"#PRP$ - Possessive pronoun (prolog version PRP-S)\n",
|
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"\"RB\": \"r\", # - Adverb\n",
|
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"\"RBR\": \"r\", # - Adverb, comparative\n",
|
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"\"RBS\": \"r\", #- Adverb, superlative\n",
|
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"#RP - Particle\n",
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"#SYM - Symbol\n",
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"#TO - to\n",
|
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"#\"UH\": \"n\", - Interjection\n",
|
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"\"VB\": \"v\", # - Verb, base form\n",
|
||||
"\"VBD\": \"v\", # - Verb, past tense\n",
|
||||
"\"VBG\": \"v\", #- Verb, gerund or present participle\n",
|
||||
"\"VBN\": \"v\", #- Verb, past participle\n",
|
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"\"VBP\": \"v\", #- Verb, non-3rd person singular present\n",
|
||||
"\"VBZ\": \"v\", #- Verb, 3rd person singular present\n",
|
||||
"#\"WDT\": \"n\" # - Wh-determiner\n",
|
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"#\"WP: - Wh-pronoun\n",
|
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"#WP$ - Possessive wh-pronoun (prolog version WP-S)\n",
|
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"\"WRB\": \"r\", # - Wh-adverb\n",
|
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" }\n",
|
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"def simplify(tag):\n",
|
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" return mapping.get(tag, \"n\")\n",
|
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"\n",
|
||||
"#Alternative:\n",
|
||||
"#from nltk.tag.simplify import simplify_wsj_tag"
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]
|
||||
},
|
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{
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"cell_type": "code",
|
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"execution_count": 442,
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"metadata": {
|
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"collapsed": false,
|
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"scrolled": false
|
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},
|
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"outputs": [
|
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{
|
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"data": {
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"image/png": [
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"gg==\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb05bc9910>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from nltk.tokenize import word_tokenize\n",
|
||||
"\n",
|
||||
"ex = \"\"\" The father of a woman who died after a savage gang-rape in Delhi said he thought everyone should watch a documentary about the attack broadcast by the BBC but banned in India \"If a man can speak like that in jail, imagine what he would say if he was walking free,\" said the father of the victim \"\"\"\n",
|
||||
"# ex = \"The bombing killed 20 people and wounded more that 30.\"\n",
|
||||
"def get_emotions(text):\n",
|
||||
" pos = nltk.pos_tag(word_tokenize(text))\n",
|
||||
" simply = [(i,simplify(j)) for i,j in pos]\n",
|
||||
"# print header\n",
|
||||
" total = None\n",
|
||||
" numwords = 0\n",
|
||||
" for (i,j) in simply:\n",
|
||||
" key = u\"{}#{}\".format(i,j)\n",
|
||||
" if key in lexdict:\n",
|
||||
" numwords += 1\n",
|
||||
" total = map(sum, zip(total, lexdict[key])) if total else lexdict[key]\n",
|
||||
"# print \"{}\\n- {}\".format(key, lexdict[key])\n",
|
||||
"# print(\"The total is\")\n",
|
||||
" minemotion = min(total)\n",
|
||||
" maxemotion = max(total)-minemotion\n",
|
||||
" total = map(lambda x: (x-minemotion)/maxemotion, total)\n",
|
||||
"# maxemotion = max(total)\n",
|
||||
"# total = map(lambda x: (x)/maxemotion, total)\n",
|
||||
"# print(total)\n",
|
||||
" return total\n",
|
||||
"total = get_emotions(ex)\n",
|
||||
"ind = np.arange(len(total))\n",
|
||||
"width = 0.5\n",
|
||||
"fig, ax = plt.subplots(1, 1)\n",
|
||||
"ax.bar(ind, total, width=width)\n",
|
||||
"ax.set_xticks(ind+width/2)\n",
|
||||
"ax.set_xticklabels(header[1:], rotation=\"vertical\")\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 434,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AFRAID 0.112249\n",
|
||||
"AMUSED 0.123500\n",
|
||||
"ANGRY 0.138816\n",
|
||||
"ANNOYED 0.116850\n",
|
||||
"DONT_CARE 0.145988\n",
|
||||
"HAPPY 0.116959\n",
|
||||
"INSPIRED 0.112220\n",
|
||||
"SAD 0.133418\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 434,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ex = \"\"\" The father of a woman who died after a savage gang-rape in Delhi said he thought everyone should watch a documentary about the attack broadcast by the BBC but banned in India \"If a man can speak like that in jail, imagine what he would say if he was walking free,\" said the father of the victim \"\"\"\n",
|
||||
"def get_emotions_pd(text):\n",
|
||||
" df = pd.DataFrame(columns=header[1:])\n",
|
||||
" pos = nltk.pos_tag(word_tokenize(text))\n",
|
||||
" simply = [(i,simplify(j)) for i,j in pos]\n",
|
||||
" for (i,j) in simply:\n",
|
||||
" key = u\"{}#{}\".format(i,j)\n",
|
||||
" if key in lexdict:\n",
|
||||
" df.loc[key] = lexdict[key]\n",
|
||||
" return df\n",
|
||||
"\n",
|
||||
"df = get_emotions_pd(ex)\n",
|
||||
"df.mean()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
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Reference in New Issue