README.md

@@ -40,9 +40,7 @@ Inclusion of a site in this model is not an endorsement of the site.
 * Epoch Times
 * The Federalist
 * Fox News
-* LifeSiteNews
 * New York Post
 * Not the Bee
 * One America News Network
-* RedState
 * Washington Examiner

analyses/constitutional_amendments.py

@@ -1,70 +0,0 @@
-import gptc
-
-amendments = [
-    ("1st", "First"),
-    ("2nd", "Second"),
-    ("3rd", "Third"),
-    ("4th", "Fourth"),
-    ("5th", "Fifth"),
-    ("6th", "Sixth"),
-    ("7th", "Seventh"),
-    ("8th", "Eighth"),
-    ("9th", "Ninth"),
-    ("10th", "Tenth"),
-    ("11th", "Eleventh"),
-    ("12th", "Twelfth"),
-    ("13th", "Thirteenth"),
-    ("14th", "Fourteenth"),
-    ("15th", "Fifteenth"),
-    ("16th", "Sixteenth"),
-    ("17th", "Seventeenth"),
-    ("18th", "Eighteenth"),
-    ("19th", "Nineteenth"),
-    ("20th", "Twentieth"),
-    ("21st", "Twenty-first"),
-    ("22nd", "Twenty-second"),
-    ("23rd", "Twenty-third"),
-    ("24th", "Twenty-fourth"),
-    ("25th", "Twenty-fifth"),
-    ("26th", "Twenty-sixth"),
-    ("27th", "Twenty-seventh"),
-]
-
-with open("model.gptc", "rb") as f:
-    model = gptc.deserialize(f)
-
-data = {}
-
-for number, name in amendments:
-    number_data = model.get(number + " Amendment")
-    name_data = model.get(name + " Amendment")
-
-    if number_data and not name_data:
-        data[name] = number_data
-    elif name_data and not number_data:
-        data[name] = name_data
-    elif number_data and name_data:
-        data[name] = {
-            key: (number_data[key] + name_data[key]) / 2
-            for key in number_data.keys()
-        }
-
-classified_amendments = sorted(data.items(), key=lambda x: x[1]["left"])
-
-print("# Constitutional Amendment Analysis")
-print()
-print("""This is an analysis of which amendments to the U.S. Constitution are mentioned
-more in right- or left-leaning American news sources. Data do not necessarily
-correlate with support or opposition for the amendment among right- or
-left-leaning Americans.""")
-print()
-print("| Amendment      | Left  | Right |")
-print("+----------------+-------+-------+")
-for amendment, data in classified_amendments:
-    percent_right = f"{data['right']*100:>4.1f}%"
-    percent_left = f"{data['left']*100:>4.1f}%"
-
-    amendment_padding = " "*(14 - len(amendment))
-    print(f"| {amendment}{amendment_padding} | {percent_left} | {percent_right} |")
-print("+----------------+-------+-------+")
-print("| Amendment      | Left  | Right |")

analyses/states.py

@@ -1,85 +0,0 @@
-import gptc
-
-states = [
-    "Alabama",
-    "Alaska",
-    "Arizona",
-    "Arkansas",
-    "California",
-    "Colorado",
-    "Connecticut",
-    "Delaware",
-    "Florida",
-    "Georgia",
-    "Hawaii",
-    "Idaho",
-    "Illinois",
-    "Indiana",
-    "Iowa",
-    "Kansas",
-    "Kentucky",
-    "Louisiana",
-    "Maine",
-    "Maryland",
-    "Massachusetts",
-    "Michigan",
-    "Minnesota",
-    "Mississippi",
-    "Missouri",
-    "Montana",
-    "Nebraska",
-    "Nevada",
-    "New Hampshire",
-    "New Jersey",
-    "New Mexico",
-    "New York",
-    "North Carolina",
-    "North Dakota",
-    "Ohio",
-    "Oklahoma",
-    "Oregon",
-    "Pennsylvania",
-    "Rhode Island",
-    "South Carolina",
-    "South Dakota",
-    "Tennessee",
-    "Texas",
-    "Utah",
-    "Vermont",
-    "Virginia",
-    "Washington",
-    "West Virginia",
-    "Wisconsin",
-    "Wyoming",
-]
-
-with open("model.gptc", "rb") as f:
-    model = gptc.deserialize(f)
-
-classified_states = []
-
-for state in states:
-    classified_states.append((state, model.get(state),))
-
-classified_states.sort(key=lambda x: x[1]["left"])
-
-longest = max([len(state) for state in states])
-
-print("# State Analysis")
-print()
-print("""This is an analysis of which states are mentioned more in right- or left-
-leaning American news sources. Results do not necessarily correlate with the
-political views of residents of the states; for example, the predominantly
-liberal state of Oregon is mentioned more in right-leaning sources than in
-left-leaning ones.""")
-print()
-print("| State          | Left  | Right |")
-print("+----------------+-------+-------+")
-for state, data in classified_states:
-    percent_right = f"{round(data['right']*1000)/10}%"
-    percent_left = f"{round(data['left']*1000)/10}%"
-
-    state_padding = " "*(longest - len(state))
-    print(f"| {state}{state_padding} | {percent_left} | {percent_right} |")
-print("+----------------+-------+-------+")
-print("| State          | Left  | Right |")

compile.py

@@ -32,11 +32,13 @@ raw_model = [
 ]
 
 with open("model.gptc", "w+b") as f:
-    gptc.compile(
+    f.write(
-        raw_model,
+        gptc.compile(
-        max_ngram_length=config["max_ngram_length"],
+            raw_model,
-        min_count=config["min_count"],
+            max_ngram_length=config["max_ngram_length"],
-    ).serialize(f)
+            min_count=config["min_count"],
+        ).serialize()
+    )
 
 con.commit()
 con.close()

compiler.toml

@@ -1,2 +1,2 @@
-max_ngram_length=5
+max_ngram_length=3
 min_count=5

export.py

@@ -28,7 +28,6 @@ os.mkdir("build")
 
 shutil.copy("articles.db", "build/articles.db")
 shutil.copy("sources.toml", "build/sources.toml")
-shutil.copy("compiler.toml", "build/compiler.toml")
 shutil.copy("model.gptc", "build/model.gptc")
 
 con = sqlite3.Connection("build/articles.db")

sources.toml

@@ -129,12 +129,6 @@ category="right"
 contains_articles=true
 name="Fox News"
 
-[lifesitenews]
-feed="https://www.lifesitenews.com/ldn/rss/headlines.xml"
-category="right"
-contains_articles=false
-name="LifeSiteNews"
-
 [not_the_bee]
 feed="https://notthebee.com/feed"
 category="right"
@@ -153,12 +147,6 @@ category="right"
 contains_articles=true
 name="One America News Network"
 
-[redstate]
-feed="https://redstate.com/feed"
-category="right"
-contains_articles=false
-name="RedState"
-
 [washington_examiner]
 feed="https://feeds.feedburner.com/dcexaminer/Politics"
 category="right"