add exp 4/5

docs/presentation.md

@@ -54,6 +54,11 @@ allsides.com <!-- .element: class="fragment" -->
 
 huggingface.com <!-- .element: class="fragment" -->
 
+Note:
+Let's get a handle on the shape of the data.
+
+The sources, size, and features of the data.
+
 ===
 
 <section data-background-iframe="https://www.memeorandum.com" data-background-interactive></section>
@@ -128,7 +133,7 @@ huggingface.com <!-- .element: class="fragment" -->
 ===
 
 # Data Structures
-Stories
+## Stories
 
 - Top level stories. <!-- .element: class="fragment" -->
     - title.
@@ -142,7 +147,7 @@ Stories
 ==
 
 # Data Structures
-Bias
+## Bias
 
 - Per publisher. <!-- .element: class="fragment" -->
     - name.
@@ -153,7 +158,7 @@ Bias
 ==
 
 # Data Structures
-Embeddings
+## Embeddings
 
 - Per story title. <!-- .element: class="fragment" -->
     - sentence embedding (n, 384).
@@ -169,7 +174,7 @@ Embeddings
 
 # Data Collection
 
-Story Scraper (simplified)
+## Story Scraper (simplified)
 
 ```python
 day = timedelta(days=1)
@@ -187,7 +192,8 @@ while cur <= end:
 ==
 
 # Data Collection
-Bias Scraper (hard)
+
+## Bias Scraper (hard)
 
 ```python
 ...
@@ -206,14 +212,16 @@ for row in rows:
 ==
 
 # Data Collection
-Bias Scraper (easy)
+
+## Bias Scraper (easy)
 
 ![allsides request](https://studentweb.cs.wwu.edu/~jensen33/static/577/allsides_request.png)
 
 ==
 
 # Data Collection
-Embeddings (easy)
+
+## Embeddings (easy)
 
 ```python
 # table = ...
@@ -230,7 +238,8 @@ for chunk in table:
 ==
 
 # Data Collection
-Classification Embeddings (medium) 
+
+## Classification Embeddings (medium) 
 
 ```python
 ...
@@ -249,7 +258,8 @@ for i, class_id in enumerate(class_ids):
 ==
 
 # Data Selection
-Stories
+
+## Stories
 
 - Clip the first and last full year of stories. <!-- .element: class="fragment" -->
 - Remove duplicate stories (big stories span multiple days). <!-- .element: class="fragment" -->
@@ -257,7 +267,7 @@ Stories
 ==
 # Data Selection
 
-Publishers
+## Publishers
 
 - Combine subdomains of stories. <!-- .element: class="fragment" -->
     - blog.washingtonpost.com and washingtonpost.com are considered the same publisher. 
@@ -267,7 +277,7 @@ Publishers
 
 # Data Selection
 
-Links
+## Links
 
 - Select only stories with publishers whose story had been a 'parent' ('original publishers'). <!-- .element: class="fragment" -->
     - Eliminates small blogs and non-original news.
@@ -279,7 +289,7 @@ Links
 
 # Data Selection
 
-Bias
+## Bias
 
 - Keep all ratings, even ones with low agree/disagree ratio.
 - Join datasets on publisher name. 
@@ -292,7 +302,7 @@ Bias
 
 # Descriptive Stats
 
-Raw
+## Raw
 
 | metric            |   value |
 |:------------------|--------:|
@@ -307,7 +317,7 @@ Raw
 ==
 # Descriptive Stats
 
-Stories Per Publisher
+## Stories Per Publisher
 
 ![stories per publisher](/static/577/stories_per_publisher.png)
 
@@ -315,7 +325,7 @@ Stories Per Publisher
 
 # Descriptive Stats
 
-Top Publishers
+## Top Publishers
 
 ![top publishers](https://studentweb.cs.wwu.edu/~jensen33/static/577/top_publishers.png)
 
@@ -323,7 +333,7 @@ Top Publishers
 
 # Descriptive Stats
 
-Articles Per Year
+## Articles Per Year
 
 ![articles per year](https://studentweb.cs.wwu.edu/~jensen33/static/577/articles_per_year.png)
 
@@ -331,7 +341,7 @@ Articles Per Year
 
 # Descriptive Stats
 
-Common TLDs
+## Common TLDs
 
 ![common tlds](https://studentweb.cs.wwu.edu/~jensen33/static/577/common_tld.png)
 
@@ -339,9 +349,9 @@ Common TLDs
 
 # Descriptive Stats
 
-Post Process
+## Post Process
 
-| key               |   value |
+| metric            |   value |
 |:------------------|--------:|
 | total stories     |  251553 |
 | total related     |  815183 |
@@ -352,6 +362,7 @@ Post Process
 | top level domains |     234 |
 
 ===
+
 # Experiments
 
 1. **clustering** on link similarity. <!-- .element: class="fragment" -->
@@ -361,9 +372,16 @@ Post Process
 5. **regression** on emotional classification over time and publication. <!-- .element: class="fragment" -->
 
 ===
+
 # Experiment 1
 
-Setup
+**clustering** on link similarity.
+
+==
+
+# Experiment 1
+
+## Setup
 
 - Create one-hot encoding of links between publishers. <!-- .element: class="fragment" -->
 - Cluster the encoding. <!-- .element: class="fragment" -->
@@ -379,7 +397,7 @@ Principle Component Analysis:
 
 # Experiment 1
 
-One Hot Encoding
+## One Hot Encoding
 
 | publisher |  nytimes|  wsj| newsweek|  ...|
 |:----------|--------:|----:|--------:|----:|
@@ -392,7 +410,7 @@ One Hot Encoding
 
 # Experiment 1
 
-n-Hot Encoding
+## n-Hot Encoding
 
 | publisher |  nytimes|  wsj| newsweek|  ...|
 |:----------|--------:|----:|--------:|----:|
@@ -405,7 +423,7 @@ n-Hot Encoding
 
 # Experiment 1
 
-Normalized n-Hot Encoding
+## Normalized n-Hot Encoding
 
 | publisher |  nytimes|  wsj| newsweek|  ...|
 |:----------|--------:|----:|--------:|----:|
@@ -418,7 +436,7 @@ Normalized n-Hot Encoding
 
 # Experiment 1
 
-Elbow criterion
+## Elbow criterion
 
 ![elbow](https://studentweb.cs.wwu.edu/~jensen33/static/577/link_cluster_elbow.png)
 
@@ -434,7 +452,7 @@ Percentage of variance explained is the ratio of the between-group variance to t
 
 # Experiment 1
 
-Link Magnitude
+## Link Magnitude
 
 ![link magnitude cluster](https://studentweb.cs.wwu.edu/~jensen33/static/577/link_pca_clusters_links.png)
 
@@ -442,7 +460,7 @@ Link Magnitude
 
 # Experiment 1
 
-Normalized
+## Normalized
 
 ![link normalized cluster](https://studentweb.cs.wwu.edu/~jensen33/static/577/link_pca_clusters_normalized.png)
 
@@ -450,7 +468,7 @@ Normalized
 
 # Experiment 1
 
-Onehot
+## One Hot
 
 ![link onehot cluster](https://studentweb.cs.wwu.edu/~jensen33/static/577/link_pca_clusters_onehot.png)
 
@@ -458,20 +476,27 @@ Onehot
 
 # Experiment 1
 
-Discussion
+## Discussion
 
 - Best encoding: One hot. <!-- .element: class="fragment" -->
-    - Clusters based on total links otherwise.
+- Clusters, but no explanation. <!-- .element: class="fragment" -->
-- Clusters, but no explanation
+- Limitation: need the link encoding to cluster. <!-- .element: class="fragment" -->
-- Limitation: need the link encoding to cluster.
     - Smaller publishers might not link very much.
+- TODO: Association Rule Mining. <!-- .element: class="fragment" -->
 
 ===
 
 # Experiment 2
 
-Setup
+**classification** on link similarity.
 
+==
+
+# Experiment 2
+
+## Setup
+
+- **clustering**. <!-- .element: class="fragment" -->
 - Create features. <!-- .element: class="fragment" -->:
     - Publisher frequency.
     - Reuse link encodings.
@@ -483,7 +508,8 @@ Note:
 
 ==
 # Experiment 2
-Descriptive stats
+
+## Descriptive stats
 
 | metric      | value     |
 |:------------|:----------|
@@ -498,7 +524,7 @@ Descriptive stats
 
 # Experiment 2
 
-PCA + Labels
+## PCA + Labels
 
 ![pca vs. bias labels](https://studentweb.cs.wwu.edu/~jensen33/static/577/pca_with_classes.png)
 
@@ -506,7 +532,7 @@ PCA + Labels
 
 # Experiment 2
 
-Discussion
+## Discussion
 
 - Link encodings (and their PCA) are useful. <!-- .element: class="fragment" -->
     - Labels are (sort of) separated and clustered.
@@ -515,7 +541,7 @@ Discussion
 
 # Experiment 2
 
-Limitations
+## Limitations
 
 - Dependent on accurate rating. <!-- .element: class="fragment" -->
 - Ordinal ratings not available. <!-- .element: class="fragment" -->
@@ -525,13 +551,260 @@ Limitations
 
 ===
 
-# Experiment 3
+# Experiment 3 
 
-Setup
+**classification** on sentence embedding.
 
 ==
 
-# Limitations
+# Experiment 3 
+
+## Setup
+
+
+- **classification**. <!-- .element: class="fragment" -->
+- Generate sentence embedding for each title. <!-- .element: class="fragment" -->
+- Rerun PCA analysis on title embeddings. <!-- .element: class="fragment" -->
+- Use kNN classifier to map embedding features to bias rating. <!-- .element: class="fragment" -->
+
+==
+
+# Experiment 3
+
+## Sentence Embeddings
+
+1. Extract titles.
+2. Tokenize titles.
+3. Pick pretrained Language Model.
+4. Generate embeddings from tokens.
+
+==
+
+# Experiment 3
+
+## Tokens
+
+**The sentence:**
+
+"Spain, Land of 10 P.M. Dinners, Asks if It's Time to Reset Clock"
+
+**Tokenizes to:**
+
+```
+['[CLS]', 'spain', ',', 'land', 'of', '10', 'p', '.', 'm', '.', 
+    'dinners', ',', 'asks', 'if', 'it', "'", 's', 'time', 'to', 
+    'reset', 'clock', '[SEP]']
+```
+
+Note:
+[CLS] is unique to BERT models and stands for classification.
+
+==
+
+# Experiment 3
+
+## Tokens
+
+**The sentence:**
+
+"NPR/PBS NewsHour/Marist Poll Results and Analysis"
+
+**Tokenizes to:**
+
+```
+['[CLS]', 'npr', '/', 'pbs', 'news', '##ho', '##ur', '/', 'maris', 
+    '##t', 'poll', 'results', 'and', 'analysis', '[SEP]', '[PAD]', 
+    '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']
+```
+
+Note:
+The padding is there to make all tokenized vectors equal length.
+
+The tokenizer also outputs a mask vector that the language model uses to ignore the padding.
+
+==
+
+# Experiment 3
+
+## Embeddings
+
+- Using a BERT (Bidirectional Encoder Representations from Transformers) based model.
+- Input: tokens.
+- Output: dense vectors representing 'semantic meaning' of tokens.
+
+==
+
+# Experiment 3
+
+## Embeddings
+
+**The tokens:**
+
+```
+['[CLS]', 'npr', '/', 'pbs', 'news', '##ho', '##ur', '/', 'maris', 
+    '##t', 'poll', 'results', 'and', 'analysis', '[SEP]', '[PAD]', 
+    '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']
+```
+
+**Embeds to a vector (1, 384):**
+
+```
+array([[ 0.12444635, -0.05962477, -0.00127911, ...,  0.13943022,
+        -0.2552534 , -0.00238779],
+       [ 0.01535596, -0.05933844, -0.0099495 , ...,  0.48110735,
+         0.1370568 ,  0.3285091 ],
+       [ 0.2831368 , -0.4200529 ,  0.10879617, ...,  0.15663117,
+        -0.29782432,  0.4289513 ],
+       ...,
+```
+==
+
+# Experiment 3
+
+## Results
+
+![pca vs. classes](https://studentweb.cs.wwu.edu/~jensen33/static/577/embedding_sentence_pca.png)
+
+Note:
+Not a lot of information in PCA this time.
+
+==
+
+# Experiment 3
+
+## Results
+
+![pca vs. avg embedding](https://studentweb.cs.wwu.edu/~jensen33/static/577/avg_embedding_sentence_pca.png) <!-- .element: class="r-stretch" -->
+
+
+Note:
+What about average publisher embedding?
+
+==
+
+# Experiment 3
+
+## Results
+
+![knn embedding confusion](https://studentweb.cs.wwu.edu/~jensen33/static/577/sentence_confusion.png)
+
+Note:
+Trained a kNN from sklearn.
+
+Set aside 20% of the data as a test set.
+
+Once trained, compared the predictions with the true on the test set.
+
+==
+
+# Experiment 3
+
+## Discussion
+
+- Embedding space is hard to condense with PCA. <!-- .element: class="fragment" -->
+- Maybe the classifier is learning to guess 'left-ish'? <!-- .element: class="fragment" -->
+
+===
+
+# Experiment 4
+
+**classification** on sentiment analysis.
+
+==
+# Experiment 4
+
+## Setup
+
+- Use pretrained Language Classifier. <!-- .element: class="fragment" -->
+- Previously: Mapped twitter posts to tokens, to embedding, to ['positive', 'negative'] labels. <!-- .element: class="fragment" -->
+- Predict: rate of neutral titles decreasing over time.
+
+==
+
+# Experiment 4
+
+## Results
+
+![sentiment over time](https://studentweb.cs.wwu.edu/~jensen33/static/577/sentiment_over_time.png)
+
+==
+# Experiment 4
+
+## Results
+
+![bias vs. sentiment over time](https://studentweb.cs.wwu.edu/~jensen33/static/577/bias_vs_sentiment_over_time.png)
+
+==
+
+# Experiment 4
+
+## Discussion
+
+-
+
+===
+
+# Experiment 5
+
+**regression** on emotional classification over time and publication.
+
+==
+# Experiment 5
+
+## Setup
+
+- Use pretrained language classifier. <!-- .element: class="fragment" -->
+- Previously: Mapped reddit posts to tokens, to embedding, to emotion labels. <!-- .element: class="fragment" -->
+- Predict: rate of neutral titles decreasing over time.
+- Classify: 
+    - features: emotional labels
+    - labels: bias
+
+==
+
+# Experiment 5
+
+## Results
+
+![emotion over time](https://studentweb.cs.wwu.edu/~jensen33/static/577/emotion_over_time.png)
+
+==
+
+# Experiment 5
+
+## Results
+
+![emotion regression time](https://studentweb.cs.wwu.edu/~jensen33/static/577/emotion_regression.png)
+
+==
+
+# Experiment 5
+
+## Discussion
+
+- Neutral story titles dominate the dataset. <!-- .element: class="fragment" -->
+- Increase in stories published might explain most of the trend. <!-- .element: class="fragment" -->
+- Far-right and far-left both became less neutral. <!-- .element: class="fragment" -->
+- Left-Center and right-center became more emotional, but also neutral. <!-- .element: class="fragment" -->
+- Not a lot of movement overall. <!-- .element: class="fragment" -->
+
+===
+
+# Experiment 6 (**TODO**)
+
+## Setup
+
+- Have a lot of features now. <!-- .element: class="fragment" -->
+    - Link PCA components.
+    - Embedding PCA components.
+    - Sentiment.
+    - Emotion.
+- Can we predict with all of them: Bias. <!-- .element: class="fragment" -->
+- End user: Is that useful? Where will I get all that at inference time? <!-- .element: class="fragment" -->
+
+===
+
+# Overall Limitations
 
 - Many different authors under the same publisher. <!-- .element: class="fragment" -->
 - Publishers use syndication. <!-- .element: class="fragment" -->

src/cli.py

@@ -12,6 +12,8 @@ if __name__ == "__main__":
     cli.add_command(scrape.parse)
     cli.add_command(scrape.load)
     cli.add_command(scrape.normalize)
+    cli.add_command(scrape.create_elections_table)
+
     import word
     # cli.add_command(word.distance)
     # cli.add_command(word.train)
@@ -30,8 +32,11 @@ if __name__ == "__main__":
     cli.add_command(emotion.normalize)
     cli.add_command(emotion.analyze)
     cli.add_command(emotion.create_table)
+
     import sentence
     cli.add_command(sentence.embed)
+    cli.add_command(sentence.create_avg_pca_table)
+
     from train import main as train_main
     cli.add_command(train_main.main)
 
@@ -54,4 +59,14 @@ if __name__ == "__main__":
     import  plots.classifier as plotc
     cli.add_command(plotc.pca_with_classes)
 
+    import plots
+    cli.add_command(plots.sentence.sentence_pca)
+    cli.add_command(plots.sentence.avg_sentence_pca)
+    cli.add_command(plots.emotion.emotion_over_time)
+    cli.add_command(plots.emotion.emotion_regression)
+
+    cli.add_command(plots.sentiment.over_time)
+    cli.add_command(plots.sentiment.bias_over_time)
+
+
     cli()

src/data/scrape.py

@@ -335,3 +335,92 @@ def another_norm():
         on sv2.id = s.id
         limit 5
     """)
+
+@click.command('data:create-election-table')
+def create_elections_table():
+    df = pd.read_csv(data_dir() / 'election_dates.csv', sep="|")
+    df['date'] = pd.to_datetime(df.date)
+
+    DB = connect()
+    DB.query("""
+        CREATE OR REPLACE TABLE election_dates AS
+        SELECT
+            row_number() over() as id
+            ,type
+            ,date
+        FROM df
+    """)
+
+    DB.query("""
+        CREATE OR REPLACE TABLE election_distance AS
+        WITH cte as (
+            SELECT
+                day(e.date - s.published_at) as days_away
+                ,e.id as election_id
+                ,e.date as election_date
+                ,s.published_at as publish_date
+            FROM (
+                SELECT
+                    DISTINCT
+                    published_at
+                FROM top.stories
+            ) s
+            CROSS JOIN election_dates e
+        ) , windowed as (
+            SELECT
+                row_number() over(partition by publish_date order by abs(days_away) asc) as rn
+                ,days_away
+                ,publish_date
+                ,election_date
+                ,election_id
+            FROM cte
+        )
+        SELECT
+            days_away
+            ,publish_date
+            ,election_date
+            ,election_id
+        FROM windowed
+        WHERE rn = 1
+    """)
+
+    DB.close()
+
+@click.command('scrape:create-denorm')
+def create_denorm():
+
+    DB = connect()
+    DB.sql("create schema denorm")
+    DB.sql("""
+        CREATE OR REPLACE TABLE denorm.stories AS
+        SELECT
+            s.id as story_id
+            ,s.title
+            ,s.url
+            ,s.published_at
+            ,s.author
+            ,p.name as publisher
+            ,p.tld as tld
+            ,sent.class_id as sentiment
+            ,d.days_away as election_distance
+            ,b.ordinal as bias
+            ,pca.first as link_1
+            ,pca.second as link_2
+            ,e.emotion_id as emotion
+        FROM top.stories s
+        JOIN top.publishers p
+        ON p.id = s.publisher_id
+        JOIN top.story_sentiments sent
+        ON s.id = sent.story_id
+        JOIN election_distance d
+        ON d.election_date = s.published_at
+        JOIN publisher_bias pb
+        ON pb.publisher_id = p.id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+        JOIN top.publisher_pca_onehot pca
+        ON pca.publisher_id = p.id
+        JOIN story_emotions e
+        ON e.story_id = s.id
+    """)
+    DB.close()

src/emotion.py

@@ -379,24 +379,34 @@ def debug():
 
 def another():
     DB = connect()
+
     DB.sql("""
         select
             *
         from emotions
     """)
 
-    emotions = DB.sql("""
+    DB.sql("""
         select
-            year(s.published_at) as year
+            *
-            ,se.label as emotion
+        from story_emotions
-            ,count(1) as stories
+    """)
-        from stories s
+
-        join story_emotions se
+    emotions = DB.sql("""
-        on s.id = se.story_id
+        SELECT
-        group by
+            YEAR(s.published_at) AS year
-            year(s.published_at)
+            ,e.label AS emotion
-            ,se.label
+            ,count(1) AS stories
+        FROM stories s
+        JOIN story_emotions se
+        ON s.id = se.story_id
+        JOIN emotions e
+        ON e.id = se.emotion_id
+        GROUP by
+            YEAR(s.published_at)
+            ,e.label
     """).df()
+    emotions
 
     sns.scatterplot(x=emotions['year'], y=emotions['stories'], hue=emotions['emotion'])
     plt.show()

src/plots/__init__.py

@@ -0,0 +1,9 @@
+import plots.sentence
+import plots.emotion
+import plots.sentiment
+
+__all__ = [
+    'sentence'
+    'emotion',
+    'sentiment',
+]

src/plots/emotion.py

@@ -0,0 +1,117 @@
+import click
+from data.main import connect
+import os
+from pathlib import Path
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+out_path = Path(os.getenv('DATA_MINING_DOC_DIR')) / 'figures'
+
+@click.command('plot:emotion-over-time')
+def emotion_over_time():
+    filename = "emotion_over_time.png"
+    DB = connect()
+
+    emotions = DB.sql("""
+        SELECT
+            date_trunc('year', s.published_at) AS year
+            ,e.label AS emotion
+            ,count(1) AS stories
+        FROM top.stories s
+        JOIN story_emotions se
+        ON s.id = se.story_id
+        JOIN emotions e
+        ON e.id = se.emotion_id
+        GROUP by
+            date_trunc('year', s.published_at)
+            ,e.label
+    """).df()
+    DB.close()
+
+    ax = sns.scatterplot(x=emotions['year'], y=emotions['stories'], hue=emotions['emotion'])
+    ax.set(title="title emotions over years", xlabel="year", ylabel="stories (#)")
+    plt.savefig(out_path / filename)
+    print(f"saved: {filename}")
+
+@click.command('plot:emotion-regression')
+def emotion_regression():
+    from sklearn import linear_model
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import ConfusionMatrixDisplay
+
+    filename = "emotion_regression.png"
+
+
+    DB = connect()
+    emotions = DB.query("""
+        SELECT
+        label
+        FROM emotions e
+    """).df()['label'].to_list()
+    DB.close()
+
+    DB = connect()
+    df = DB.sql(f"""
+        SELECT
+            epoch(date_trunc('yearweek', s.published_at)) AS date
+            ,e.id AS emotion_id
+            ,p.id as publisher_id
+            ,count(1) AS stories
+        FROM top.stories s
+        JOIN top.publishers p
+        ON p.id = s.publisher_id
+        JOIN story_emotions se
+        ON s.id = se.story_id
+        JOIN emotions e
+        ON e.id = se.emotion_id
+        GROUP by
+            epoch(date_trunc('yearweek', s.published_at))
+            ,p.id
+            ,e.id
+    """).df()
+    DB.close()
+
+    results = []
+    for (emotion_id, publisher_id), group in df.groupby(['emotion_id', 'publisher_id']):
+        model = linear_model.LinearRegression()
+        x = group['date'].to_numpy().reshape(-1, 1)
+        y = group['stories'].to_numpy()
+        model.fit(x, y)
+        per_year = model.coef_.item() * 60 * 60 * 24 * 365
+        results.append({'emotion_id' : emotion_id, 'publisher_id':publisher_id, 'per_year' : per_year})
+    results = pd.DataFrame(results)
+
+    DB = connect()
+    out = DB.query("""
+        SELECT
+            e.label as emotion
+            --,p.tld
+            ,avg(results.per_year) as avg_reg_coef
+            ,b.ordinal
+        FROM results
+        JOIN emotions e
+        ON e.id = results.emotion_id
+        JOIN top.publishers p
+        ON p.id = results.publisher_id
+        JOIN publisher_bias pb
+        ON pb.publisher_id = results.publisher_id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+        GROUP BY
+            e.label
+            ,b.ordinal
+    """).df()
+    DB.close()
+    pivot = out.pivot(index=['emotion'], columns=['ordinal'], values=['avg_reg_coef'])
+
+    ax = sns.heatmap(pivot, cmap='RdBu_r')
+    ticklabels = ['left', 'left-center', 'center', 'right-center', 'right']
+    ax.set(title="slope of regression (stories/year) by bias and emotion"
+           ,xticklabels=ticklabels
+           ,xlabel="bias"
+           ,ylabel="emotion")
+    plt.tight_layout()
+    plt.savefig(out_path / filename)
+    print(f"saved: {filename}")

src/plots/sentence.py

@@ -0,0 +1,111 @@
+import click
+from data.main import connect
+import os
+from pathlib import Path
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+out_path = Path(os.getenv('DATA_MINING_DOC_DIR')) / 'figures'
+data_path = Path(os.getenv('DATA_MINING_DATA_DIR'))
+
+@click.command('plot:sentence-pca')
+def sentence_pca():
+    filename = "embedding_sentence_pca.png"
+    DB = connect()
+
+    data = DB.query("""
+        SELECT
+            pca.first
+            ,pca.second
+            ,b.bias as label
+        FROM top.story_embeddings_pca pca
+        JOIN top.stories s
+        ON s.id = pca.story_id
+        JOIN top.publisher_bias pb
+        ON pb.publisher_id = s.publisher_id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+    """).df()
+    DB.close()
+
+    ax = sns.scatterplot(x=data['first'], y=data['second'], hue=data['label'])
+    ax.set(title="pca components vs. bias label", xlabel="first component", ylabel="second component")
+    plt.savefig(out_path / filename)
+
+@click.command('plot:avg-sentence-pca')
+def avg_sentence_pca():
+    filename = "avg_embedding_sentence_pca.png"
+    DB = connect()
+
+    data = DB.query("""
+        SELECT
+            pca.first
+            ,pca.second
+            ,p.tld
+            ,b.bias as label
+        FROM top.publisher_embeddings_pca pca
+        JOIN top.publishers p
+        ON p.id = pca.publisher_id
+        JOIN top.publisher_bias pb
+        ON pb.publisher_id = p.id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+    """).df()
+    DB.close()
+
+    ax = sns.scatterplot(x=data['first'], y=data['second'], hue=data['label'])
+    ax.set(title="avg. publisher embedding pca components vs. bias label", xlabel="first component", ylabel="second component")
+    plt.savefig(out_path / filename)
+
+@click.command('plot:sentence-confusion')
+def sentence_confusion():
+    from sklearn.model_selection import train_test_split
+    from sklearn.neighbors import KNeighborsClassifier
+    from sklearn.metrics import ConfusionMatrixDisplay
+
+    filename = "sentence_confusion.png"
+
+    embeddings = np.load(data_path / 'embeddings.npy')
+    embedding_ids = np.load(data_path / 'embedding_ids.npy')
+    ids = pd.DataFrame(embedding_ids, columns=['story_id']).reset_index()
+
+    DB = connect()
+    data = DB.query("""
+        SELECT
+            ids.index
+            ,s.id
+            ,b.ordinal
+        FROM ids
+        JOIN top.stories s
+        ON ids.story_id = s.id
+        JOIN top.publisher_bias pb
+        ON pb.publisher_id = s.publisher_id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+    """).df()
+    pub = DB.query("""
+        SELECT
+            *
+        FROM top.publishers
+    """).df()
+    DB.close()
+
+    train, test = train_test_split(data)
+    train_x, train_y = embeddings[train['index']], train['ordinal']
+    test_x, test_y = embeddings[test['index']], test['ordinal']
+
+    model = KNeighborsClassifier(n_neighbors=5)
+    model.fit(train_x, train_y)
+    pred = model.predict(test_x)
+
+
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ConfusionMatrixDisplay.from_predictions(test_y, pred, ax=ax)
+    ticklabels = ['left', 'left-center', 'center', 'right-center', 'right']
+    ax.set(title="confusion matrix for kNN classifier on test data.", xticklabels=ticklabels, yticklabels=ticklabels)
+    plt.savefig(out_path / filename)
+    plt.close()
+
+    print(f"saved plot: {filename}")

src/plots/sentiment.py

@@ -0,0 +1,60 @@
+import click
+from data.main import connect
+import os
+from pathlib import Path
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+out_path = Path(os.getenv('DATA_MINING_DOC_DIR')) / 'figures'
+@click.command('plot:sentiment-over-time')
+def over_time():
+    filename = "sentiment_over_time.png"
+
+    DB = connect()
+    data = DB.sql("""
+        SELECT
+            avg(sent.class_id) as sentiment
+            ,s.published_at as date
+        FROM top.story_sentiments sent
+        JOIN top.stories s
+        ON s.id = sent.story_id
+        GROUP BY
+            s.published_at
+    """).df()
+    DB.close()
+
+    ax = sns.scatterplot(x=data['date'], y=data['sentiment'])
+    ax.set(title="sentiment vs. time")
+    plt.tight_layout()
+    plt.savefig(out_path / filename)
+    print(f"saved: {filename}")
+@click.command('plot:bias-vs-sentiment-over-time')
+def bias_over_time():
+    filename = "bias_vs_sentiment_over_time.png"
+
+    DB = connect()
+    data = DB.sql("""
+        SELECT
+            avg(sent.class_id) as sentiment
+            ,s.published_at as date
+            ,b.id as bias_id
+        FROM top.story_sentiments sent
+        JOIN top.stories s
+        ON s.id = sent.story_id
+        JOIN publisher_bias pb
+        ON pb.publisher_id = s.publisher_id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+        GROUP BY
+            s.published_at
+            ,b.id
+    """).df()
+    DB.close()
+
+    ax = sns.relplot(x=data['date'], y=data['sentiment'], col=data['bias_id'])
+    ax.set(title="sentiment vs. time grouped by bias")
+    plt.tight_layout()
+    plt.savefig(out_path / filename)
+    print(f"saved: {filename}")

src/sentence.py

@@ -72,16 +72,71 @@ def embed(chunks):
     print(f"ids saved: {save_to}")
 
 
-@click.command('sentence:create-pca-table')
+@click.command('sentence:create-avg-pca-table')
-def create_table():
+def create_avg_pca_table():
-    from sklearn import linear_model
+    from sklearn.decomposition import PCA
     data_path = Path(os.getenv('DATA_MINING_DATA_DIR'))
 
+
     embeddings = np.load(data_path / 'embeddings.npy')
     embedding_ids = np.load(data_path / 'embedding_ids.npy')
     ids = pd.DataFrame(embedding_ids, columns=['story_id']).reset_index()
-    DB = connect()
 
+    DB = connect()
+    data = DB.query("""
+        SELECT
+            ids.index
+            ,s.id
+            ,s.publisher_id
+            ,b.ordinal
+        FROM ids
+        JOIN top.stories s
+        ON ids.story_id = s.id
+        JOIN top.publisher_bias pb
+        ON pb.publisher_id = s.publisher_id
+        JOIN bias_ratings b
+        ON b.id = pb.bias_id
+    """).df()
+    DB.close()
+
+    results = []
+    for publisher_id, group in data.groupby(['publisher_id']):
+        avg = embeddings[group['index']].mean(axis=0)
+        ordinal = group['ordinal'].iloc[0]
+        results.append({'publisher_id' : publisher_id, 'embedding' : avg, 'ordinal' : ordinal})
+    results = pd.DataFrame(results)
+
+    x = np.stack(results['embedding'])
+    y = results['ordinal']
+
+    model = PCA(n_components=2)
+    pred = model.fit_transform(x)
+    results['first'] = pred[:, 0]
+    results['second'] = pred[:, 1]
+
+    table_name = "top.publisher_embeddings_pca"
+    DB = connect()
+    DB.query(f"""
+        CREATE OR REPLACE TABLE {table_name} AS
+        SELECT
+            results.publisher_id as publisher_id
+            ,results.first as first
+            ,results.second as second
+        FROM results
+    """)
+    DB.close()
+    print(f"created {table_name}")
+
+
+@click.command('sentence:create-pca-table')
+def create_pca_table():
+    from sklearn.decomposition import PCA
+    data_path = Path(os.getenv('DATA_MINING_DATA_DIR'))
+
+    embeddings = np.load(data_path / 'embeddings.npy')
+    embedding_ids = np.load(data_path / 'embedding_ids.npy')
+
+    DB = connect()
     data = DB.query("""
         SELECT
             ids.index
@@ -95,19 +150,38 @@ def create_table():
         JOIN bias_ratings b
         ON b.id = pb.bias_id
     """).df()
+    pub = DB.query("""
+        SELECT
+            *
+        FROM top.publishers
+    """).df()
+    DB.close()
 
     x = embeddings[data['index']]
     y = data['ordinal'].to_numpy().reshape(-1, 1)
+    model = PCA(n_components=2)
+    pred = model.fit_transform(x)
+    data['first'] = pred[:, 0]
+    data['second'] = pred[:, 1]
 
-    reg = linear_model.LinearRegression()
+    table_name = f"top.story_embeddings_pca"
 
-    reg.fit(x, y)
+    DB = connect()
-
+    DB.query(f"""
-    reg.coef_.shape
+        CREATE OR REPLACE TABLE {table_name} AS
+        SELECT
+            data.id as story_id
+            ,data.first as first
+            ,data.second as second
+        FROM data
+    """)
+    DB.close()
+    print(f"created {table_name}")
 
 @click.command('sentence:create-svm-table')
 def create_svm_table():
     from sklearn import svm
+    from sklearn.linear_model import SGDClassifier
     data_path = Path(os.getenv('DATA_MINING_DATA_DIR'))
 
     embeddings = np.load(data_path / 'embeddings.npy')
@@ -133,6 +207,8 @@ def create_svm_table():
     #y = data['ordinal'].to_numpy().reshape(-1, 1)
     y = data['ordinal']
 
-    clf = svm.SVC()
+    model = SGDClassifier()
-    pred = clf.fit(x, y)
+    pred = model.fit(x, y)
+    data['pred'] = pred.predict(x)
+    data
 

src/sentiment.py

@@ -0,0 +1,86 @@
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+import torch.nn.functional as F
+from data import connect, data_dir
+import numpy as np
+from tqdm import tqdm
+import click
+
+@click.option('-c', '--chunks', type=int, default=500, show_default=True)
+@click.command("sentiment:extract")
+def extract(chunks):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    chunks = 1000
+
+    # Load model from HuggingFace Hub
+    tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+    model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
+    model = model.to(device)
+
+
+    # load data
+    DB = connect()
+    table = DB.sql("""
+        select
+        id
+        ,title
+        from stories
+        order by id desc
+    """).df()
+    DB.close()
+
+    # normalize text
+    table['title'] = table['title'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')
+
+
+    chunked = np.array_split(table, chunks)
+
+    # generate embeddings from list of titles
+    iterator = tqdm(chunked, 'embedding')
+    sentiments = []
+    story_ids = []
+    for _, chunk in enumerate(iterator):
+        sentences = chunk['title'].tolist()
+        ids = chunk['id'].tolist()
+        # Tokenize sentences
+        encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+        # Compute token embeddings
+        with torch.no_grad():
+            logits = model(**encoded_input.to(device)).logits
+        sentiment = logits.argmax(axis=1).tolist()
+        sentiments.append(sentiment)
+        story_ids.append(ids)
+
+    sentiments = np.concatenate(sentiments)
+    story_ids = np.concatenate(story_ids)
+
+    # save embeddings
+    save_to = data_dir() / 'sentiment.npy'
+    np.save(save_to, sentiments)
+    print(f"sentiments saved: {save_to}")
+
+    # save ids
+    save_to = data_dir() / 'sentiment_ids.npy'
+    np.save(save_to, story_ids)
+    print(f"ids saved: {save_to}")
+
+@click.command('sentiment:load')
+def load():
+
+    DB = connect()
+    sentiments = np.load(data_dir() / 'sentiment.npy')
+    story_ids = np.load(data_dir() / 'sentiment_ids.npy')
+    data = pd.DataFrame(story_ids, columns=['story_id']).reset_index()
+    data['sentiment_id'] = sentiments
+
+    DB.query("""
+        CREATE OR REPLACE TABLE top.story_sentiments AS
+        SELECT
+            data.story_id
+            ,data.sentiment_id as class_id
+            ,CASE WHEN data.sentiment_id = 1 THEN 'positive' ELSE 'negative' end as label
+        FROM data
+        JOIN top.stories s
+        ON s.id = data.story_id
+    """)
+    DB.close()