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Climate Change Topic Modelling
Raw
README.md

After obtaining articles from the New York Times that included the phrases 'climate change' and 'global warming' between 1988 - 2016, Dynamic Topic Modeling was applied to search for shifts in the discussion of climate change over time. Analyses were performed using the Python package 'gensim', which provides a wrapper for a C+ based program based on that originally developed by David Blei (https://radimrehurek.com/gensim/models/wrappers/dtmmodel.html). A total of 8 coherent topics were identified and labled. The top 10 words from each year were then identified and used to generate streamplots using d3.js.

Streamplots aren't the perfect visualization, but they do provide a useful means of assessing the general importance of particular words within each topic and how the use of those words, within that topic, and within the broader umbrella of "Climate Change' has shifted over time.

Raw
index.html
<!DOCTYPE html>
<html lang='en'>
<head>
<meta charset="utf-8">
<title> Climate Streamplot - Topic 1</title>
<script src="http://d3js.org/d3.v3.js"></script>
<style type = 'text/css'>
body {
font-family: "Palatino Linotype", "Book Antiqua", Palatino, serif;
background-color: white;
padding: 50px;
}
h1 {
font-size: 28px;
position: relative;
left: 100;
color: #525252;
}
h2 {
font-size: 20px;
position: relative;
left: 100;
color: darkgrey;
}
.chart {
background: #fff;
}
p {
font-family: "Palatino Linotype", "Book Antiqua", Palatino, serif;
font-size: 18px;
margin: 10px 0 0 50px;
}
.tooltip {
font-family: "Palatino Linotype", "Book Antiqua", Palatino, serif;
}
.axis path, .axis line {
fill: none;
stroke: #000;
stroke-width: 2px;
shape-rendering: crispEdges;
}
button {
position: absolute;
right: 50px;
top: 10px;
}
</style>
</head>
<body>
<h2 style="margin-left:3em;">Climate Change Topic Streamplot</h2>
<h1 style="margin-left:2em;">Topic 1 - Science / Nature / Species </h1>
<p> Using articles from the NY Times and Dynamic Topic Modelling to assess how the discussion of 'climate change' and 'global warming' have changed over time. </p>
<div class="chart"></div>
<script type = 'text/javascript'>
chart("topic1_stream_R.csv", "yelblu");
var datearray = [];
var colorrange = [];
function chart(csvpath, color) {
if (color == "blue") {
colorrange = ['#00441b','#006d2c','#238b45','#41ae76','#66c2a4','#99d8c9','#e5f5f9','#ccece6'];
}
else if (color == "yelblu") {
colorrange = ['#084081','#0868ac','#2b8cbe','#4eb3d3','#7bccc4','#a8ddb5','#ccebc5'];
}
else if (color == "orange") {
colorrange = ["#7f0000","#B30000", "#E34A33", "#FC8D59", "#FDBB84", "#FDD49E", "#FEF0D9"];
}
strokecolor = colorrange[0];
var format = d3.time.format("%Y");
var margin = {top: 20, right: 100, bottom: 30, left: 100};
var width = document.body.clientWidth - margin.left - margin.right;
var height = 400 - margin.top - margin.bottom;
var tooltip = d3.select("body")
.append("div")
.attr("class", "remove")
.style("position", "absolute")
.style("z-index", "20")
.style("visibility", "hidden")
.style("top", "625px")
.style("left", "180px");
var x = d3.time.scale()
.range([0, width]);
var y = d3.scale.linear()
.range([height-10, 2]);
var z = d3.scale.ordinal()
.range(colorrange);
var xAxis = d3.svg.axis()
.scale(x)
.orient("bottom")
.ticks(d3.time.twoyears);
var yAxis = d3.svg.axis()
.scale(y);
var yAxisr = d3.svg.axis()
.scale(y);
var stack = d3.layout.stack()
.offset("silhouette")
.values(function(d) { return d.values; })
.x(function(d) { return d.date; })
.y(function(d) { return d.value; });
var nest = d3.nest()
.key(function(d) { return d.key; });
var area = d3.svg.area()
.interpolate("cardinal")
.x(function(d) { return x(d.date); })
.y0(function(d) { return y(d.y0); })
.y1(function(d) { return y(d.y0 + d.y); });
var svg = d3.select(".chart").append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
var graph = d3.csv(csvpath, function(data) {
data.forEach(function(d) {
d.date = format.parse(d.date);
d.value = +d.value;
});
var layers = stack(nest.entries(data));
x.domain(d3.extent(data, function(d) { return d.date; }));
y.domain([0, d3.max(data, function(d) { return d.y0 + d.y; })]);
svg.selectAll(".layer")
.data(layers)
.enter().append("path")
.attr("class", "layer")
.attr("d", function(d) { return area(d.values); })
.style("fill", function(d, i) { return z(i); });
svg.append("g")
.attr("class", "x axis")
.attr("transform", "translate(0," + height + ")")
.call(xAxis);
svg.append("g")
.attr("class", "y axis")
.attr("transform", "translate(" + width + ", 0)")
.call(yAxis.orient("right"));
svg.append("g")
.attr("class", "y axis")
.call(yAxis.orient("left"))
.append("text")
.attr("transform", "rotate(-90)")
.attr("y",-margin.top*3.3) //bigger mulitplier moves to the left
.attr("x",-height/2) //smaller divider moves up
.attr("dy", ".71em")
.style("text-anchor", "middle")
.text("Probaility of Word | Topic")
.attr("font-family","serif")
.attr("font-size","16px");;
svg.selectAll(".layer")
.attr("opacity", 1)
.on("mouseover", function(d, i) {
svg.selectAll(".layer").transition()
.duration(250)
.attr("opacity", function(d, j) {
return j != i ? 0.6 : 1;
})})
.on("mousemove", function(d, i) {
mousex = d3.mouse(this);
mousex = mousex[0];
var invertedx = x.invert(mousex);
invertedx = invertedx.getYear() ;
var selected = (d.values);
for (var k = 0; k < selected.length; k++) {
datearray[k] = selected[k].date
datearray[k] = datearray[k].getYear() ;
}
var tooltips = d3.select('svg').append('g');
mousedate = datearray.indexOf(invertedx);
pro = d.values[mousedate].value;
d3.select(this)
.classed("hover", true)
.attr("stroke", strokecolor)
.attr("stroke-width", "1.5px"),
tooltip.html( "<p>" + d.key + "<br>" + pro + "</p>" ).style("visibility", "visible");
})
.on("mouseout", function(d, i) {
svg.selectAll(".layer")
.transition()
.duration(250)
.attr("opacity", "1");
d3.select(this)
.classed("hover", false)
.attr("stroke-width", "0px"),
tooltip.html( "<p>" + d.key + "<br>" + pro + "</p>" ).style("visibility", "hidden");
})
.on("click", function(d, i) {
svg.selectAll(".layer").transition()
.duration(250)
.attr("opacity", function(d, j) {
return j != i ? 0.6 : 1;
});
})
// .on("click", function(d, i) {
// d3.select(this)
// .transition()
// .duration(250)
// .style('fill', function(d, i) {
// if(this.style.fill = 'red') {
// return z(this);
// } else {
// return 'red';
// } });
// });
var vertical = d3.select(".chart")
.append("div")
.attr("class", "remove")
.style("position", "absolute")
.style("z-index", "19")
.style("width", "1px")
.style("height", "350px")
.style("top", "230px")
.style("bottom", "30px")
.style("left", "0px")
.style("background", "#fff");
d3.select(".chart")
.on("mousemove", function(){
mousex = d3.mouse(this);
mousex = mousex[0] + 5;
vertical.style("left", (mousex+50) + "px" )})
.on("mouseover", function(){
mousex = d3.mouse(this);
mousex = mousex[0] + 5;
vertical.style("left", (mousex+50) + "px")});
});
}
</script>
<br>
<br>
<p align = "right", style="font-size:93%;"><a style="color:#969696;", href=http://bl.ocks.org/WillTurman/4631136/>Inspired by http://bl.ocks.org/WillTurman/4631136</a></p>
<p align = "right", style="font-size:93%;"><a style="color:#969696;", href=http://bl.ocks.org/natemiller/raw/07507ddbe4b4da3b3604f4e4654a522a/>Topic #2</a></p>
<br>
<br>
<p style="font-size:93%;">Brief methods: New York Times (NYT) articles were accessed through the NYT API using the search terms 'climate change' and 'global warming' for the years January 1988 through March 2016. After tokenizing the data, removing very common words, and words that only occurred a single time, a Dynamic Topic Modeling analysis was performed using the Python package 'gensim'. Eight different topics and the words most associated with those topics over time were identified. Visualizations of the topics themselves, and the words associated with them, were created to aid in identifying how the discussion of global climate has shifted. Dynamic Topic Modeling is an exploratory analysis tool, attempting to identify latent or hidden features in text data. As such the results should be viewed as suggesting areas of additional emphasis rather than any causal or diffinitive relationships. Additionally, these data were drawn from the New York Times. Text from other publications would likely show different, but likely interesting patterns. </p>
</body>
</html>
Raw
topic1_stream_R.csv
key date value
1 also 1988 0
2 also 1989 0
3 also 1990 0
4 also 1991 0
5 also 1992 0
6 also 1993 0
7 also 1994 0
8 also 1995 0
9 also 1996 0
10 also 1997 0
11 also 1998 0
12 also 1999 0
13 also 2000 0
14 also 2001 0
15 also 2002 0
16 also 2003 0
17 also 2004 0
18 also 2005 0
19 also 2006 0
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25 also 2012 0
26 also 2013 0
27 also 2014 0
28 also 2015 0
29 also 2016 0
30 change 1988 0
31 change 1989 0
32 change 1990 0
33 change 1991 0
34 change 1992 0
35 change 1993 0
36 change 1994 0
37 change 1995 0
38 change 1996 0
39 change 1997 0
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41 change 1999 0
42 change 2000 0
43 change 2001 0
44 change 2002 0
45 change 2003 0
46 change 2004 0
47 change 2005 0
48 change 2006 0
49 change 2007 0
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52 change 2010 0
53 change 2011 0
54 change 2012 0
55 change 2013 0
56 change 2014 0
57 change 2015 0
58 change 2016 0
59 climate 1988 0.00300679
60 climate 1989 0.003005982
61 climate 1990 0.002993911
62 climate 1991 0
63 climate 1992 0
64 climate 1993 0
65 climate 1994 0
66 climate 1995 0
67 climate 1996 0
68 climate 1997 0
69 climate 1998 0
70 climate 1999 0
71 climate 2000 0
72 climate 2001 0
73 climate 2002 0
74 climate 2003 0
75 climate 2004 0
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77 climate 2006 0
78 climate 2007 0
79 climate 2008 0.002371964
80 climate 2009 0.002438772
81 climate 2010 0.002489815
82 climate 2011 0.002566014
83 climate 2012 0.002618766
84 climate 2013 0.002683698
85 climate 2014 0.002737529
86 climate 2015 0.00278965
87 climate 2016 0.002825979
88 could 1988 0
89 could 1989 0
90 could 1990 0
91 could 1991 0
92 could 1992 0
93 could 1993 0.002983597
94 could 1994 0.003038624
95 could 1995 0.003114411
96 could 1996 0.003200442
97 could 1997 0.003287686
98 could 1998 0.003363553
99 could 1999 0.003409498
100 could 2000 0.003403096
101 could 2001 0.003386183
102 could 2002 0.003395748
103 could 2003 0.003414721
104 could 2004 0.003433487
105 could 2005 0.003447933
106 could 2006 0.003456503
107 could 2007 0.003471902
108 could 2008 0.003486586
109 could 2009 0.003485596
110 could 2010 0.003476044
111 could 2011 0.003517443
112 could 2012 0.00357138
113 could 2013 0.003563497
114 could 2014 0.003539398
115 could 2015 0.003522483
116 could 2016 0.003505515
117 fish 1988 0
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143 fish 2014 0.002455589
144 fish 2015 0.002604009
145 fish 2016 0.002597072
146 forest 1988 0.003298933
147 forest 1989 0.003319259
148 forest 1990 0.003162896
149 forest 1991 0.003085312
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151 forest 1993 0
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153 forest 1995 0
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155 forest 1997 0
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169 forest 2011 0.002428008
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173 forest 2015 0
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175 global 1988 0.003232382
176 global 1989 0.00327385
177 global 1990 0.003321473
178 global 1991 0.00335173
179 global 1992 0.00335119
180 global 1993 0.003357254
181 global 1994 0.003363031
182 global 1995 0.003360915
183 global 1996 0.003315372
184 global 1997 0.003212775
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219 ice 2003 0.00480878
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222 ice 2006 0.003127642
223 ice 2007 0.00265742
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313 may 2010 0.002463764
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315 may 2012 0.002453143
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317 may 2014 0.00247223
318 may 2015 0.002495439
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383 scientists 1993 0.007300607
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386 scientists 1996 0.006348963
387 scientists 1997 0.006025537
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391 scientists 2001 0.005030151
392 scientists 2002 0.004672405
393 scientists 2003 0.004332294
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402 scientists 2012 0.003971003
403 scientists 2013 0.003977858
404 scientists 2014 0.003957874
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446 species 1998 0.003837408
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448 species 2000 0.003762489
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463 species 2015 0.005283629
464 species 2016 0.0051935
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510 university 2004 0
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512 university 2006 0
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517 university 2011 0
518 university 2012 0
519 university 2013 0
520 university 2014 0
521 university 2015 0
522 university 2016 0
523 warming 1988 0
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527 warming 1992 0
528 warming 1993 0
529 warming 1994 0
530 warming 1995 0
531 warming 1996 0.002973718
532 warming 1997 0.002984067
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534 warming 1999 0.002959533
535 warming 2000 0.002886844
536 warming 2001 0.002810899
537 warming 2002 0
538 warming 2003 0
539 warming 2004 0
540 warming 2005 0
541 warming 2006 0
542 warming 2007 0
543 warming 2008 0
544 warming 2009 0
545 warming 2010 0
546 warming 2011 0
547 warming 2012 0
548 warming 2013 0
549 warming 2014 0
550 warming 2015 0
551 warming 2016 0
552 water 1988 0.004602393
553 water 1989 0.004640839
554 water 1990 0.004676999
555 water 1991 0.004517641
556 water 1992 0.00435939
557 water 1993 0.004287745
558 water 1994 0.004139385
559 water 1995 0.003952685
560 water 1996 0.003803308
561 water 1997 0.003675572
562 water 1998 0.00353447
563 water 1999 0.003418345
564 water 2000 0.003207756
565 water 2001 0.002965084
566 water 2002 0.00276964
567 water 2003 0.00255926
568 water 2004 0
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570 water 2006 0
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573 water 2009 0
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592 world 1999 0
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609 world 2016 0.002516844
610 year 1988 0
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613 year 1991 0
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617 year 1995 0
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619 year 1997 0
620 year 1998 0
621 year 1999 0
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