Merge pull request #2 from jacquessham/v2_1_0

Upgrade to v2.1.0

.gitignore

@@ -3,7 +3,6 @@
 
 # Repository-specific
 *.html
-*.csv
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

Archive/README.md

@@ -1,32 +1,7 @@
-# (Deprecated) Japanese Whisky Reviews - Version 1.0
+# Archive
 
-There is a Japanese Whisky Review data set available in Kaggle, that the data set is  originated from Master of Malt. I am interested in doing some NLP works on this data set. <br>
-I will be making some analysis on the sentiment of the reviews and try to summarize the individual review.
-<br>
+## Version 1
+Released in 2019. You may find the scripts in the [Version 1](/v1_0_0) folder.
 
-## Tools
-In this project, I will be using packages like SKlearn, vaderSentiment, ntlk for sentiment scores and TF-IDF. <br>
-
-## Data set
-The data set could be found in <a href="https://www.kaggle.com/koki25ando/japanese-whisky-review">Kaggle</a>. It consists of 4 columns including, bottle label, brand name, title of the review and the review content. The data set only covers 4 Japanese whisky brands -- Yamazaki, Hibiki, Hakushu, and Nikka.
-
-## Sentiment Analysis
-The first task is to understand the sentiment scores across brands.
-<br>
-First, I have used vaderSentiment to calculate the sentiment score for each review. Then, I used Plotly to visualize the range of sentiment score of each brand with a boxplot. It looks like this. <br><br>
-![Screenshot](sentiment_score_boxplot.png)
-<br>
-From the boxplot, we can learn that reviewers in general have a positive view on the Japanese whiskies, while they have better impression on Nikka and Hibiki. Interestingly, the median sentiment score on Yamazaki is 0, which means neutral.
-<br>
-You may find the code <a href="jpwhisky_review_sentiment.py">here</a>
-
-## TF-IDF
-The second task is to build a model that shows the summary by displaying the top 5 key words in the review. To do this, I use TfidfVectorizer from sklearn.feature_extraction.text to build the model. To preprocess the texts, I used the same package to remove English stop words and nltk to stem the words.
-<br>
-There are 2 files of code for this task: <a href="jpwhisky_review_tfidf.py">jpwhisky_review_tfidf.py</a> is the backend, and <a href="driver.py">driver.py</a> will provoke the implementation and display result.
-<br><br>
-I don't like the first version of the displaying system because it shows the stemmed word as summary, like this:<br><br>
-![Screenshot](display_before.png)
-<br><br>
-I add a feature that once the model produce the result for a review post, driver.py will grab the original post and pair the word from original post with the stemmed word in a dictionary, then it will display the word from original post, like this:<br><br>
-![Screenshot](display_after.png)
+## Version 2.0.0
+Release on 14 July 2023. This version upgrade the project with a Dashboard powered by Plotly Dash. You may find the scripts in the [Version 2.0](v2_0_0) folder.

Archive/v1_0_0/README.md

@@ -0,0 +1,32 @@
+# (Deprecated) Japanese Whisky Reviews - Version 1.0
+
+There is a Japanese Whisky Review data set available in Kaggle, that the data set is  originated from Master of Malt. I am interested in doing some NLP works on this data set. <br>
+I will be making some analysis on the sentiment of the reviews and try to summarize the individual review.
+<br>
+
+## Tools
+In this project, I will be using packages like SKlearn, vaderSentiment, ntlk for sentiment scores and TF-IDF. <br>
+
+## Data set
+The data set could be found in <a href="https://www.kaggle.com/koki25ando/japanese-whisky-review">Kaggle</a>. It consists of 4 columns including, bottle label, brand name, title of the review and the review content. The data set only covers 4 Japanese whisky brands -- Yamazaki, Hibiki, Hakushu, and Nikka.
+
+## Sentiment Analysis
+The first task is to understand the sentiment scores across brands.
+<br>
+First, I have used vaderSentiment to calculate the sentiment score for each review. Then, I used Plotly to visualize the range of sentiment score of each brand with a boxplot. It looks like this. <br><br>
+![Screenshot](sentiment_score_boxplot.png)
+<br>
+From the boxplot, we can learn that reviewers in general have a positive view on the Japanese whiskies, while they have better impression on Nikka and Hibiki. Interestingly, the median sentiment score on Yamazaki is 0, which means neutral.
+<br>
+You may find the code <a href="jpwhisky_review_sentiment.py">here</a>
+
+## TF-IDF
+The second task is to build a model that shows the summary by displaying the top 5 key words in the review. To do this, I use TfidfVectorizer from sklearn.feature_extraction.text to build the model. To preprocess the texts, I used the same package to remove English stop words and nltk to stem the words.
+<br>
+There are 2 files of code for this task: <a href="jpwhisky_review_tfidf.py">jpwhisky_review_tfidf.py</a> is the backend, and <a href="driver.py">driver.py</a> will provoke the implementation and display result.
+<br><br>
+I don't like the first version of the displaying system because it shows the stemmed word as summary, like this:<br><br>
+![Screenshot](display_before.png)
+<br><br>
+I add a feature that once the model produce the result for a review post, driver.py will grab the original post and pair the word from original post with the stemmed word in a dictionary, then it will display the word from original post, like this:<br><br>
+![Screenshot](display_after.png)

Archive/v2_0_0/README.md

@@ -0,0 +1,54 @@
+# Japanese Whisky Reviews
+
+There is a Japanese Whisky Review data set available in Kaggle, that the data set is  originated from Master of Malt. I am interested in doing some NLP works on this data set. <br><br>
+I will be making some analysis on the sentiment of the reviews and try to summarize the individual review.
+
+## Tools
+In this project, I will be using packages like SKlearn, vaderSentiment, ntlk for sentiment scores and TF-IDF. Then, we will display the result on a dashboard via Plotly Dash.
+
+## Data set
+The data set could be found in <a href="https://www.kaggle.com/koki25ando/japanese-whisky-review">Kaggle</a>. It consists of 4 columns including, bottle label, brand name, title of the review and the review content. The data set only covers 4 Japanese whisky brands -- Yamazaki, Hibiki, Hakushu, and Nikka.
+
+
+## Dashboard
+The dashboard consists of two parts: <b>Sentiment Analysis</b> and <b>TF-IDF Analysis</b> (Core meaning of a posted comment). The Sentiment Analysis is plotted with a static box plot of sentiment scores distribution by whiksy brand. The bottom has 4 tabs represent each whisky brand. You may click on one whisky and the dashboard would randomly pick a comment and display the core meaning.
+<br><br>
+The dashboard looks like this:
+
+<img src=jp_whisky_dashboard.png>
+
+### How to Run the Dashboard?
+After ensuring installing all the dependencies, you run simply execute:
+
+```
+python viz.py
+```
+
+Once the dashboard is ready, you may access it at <b>127.0.0.1:9000</b>
+
+## Technical Explanation
+### Sentiment Analysis
+We will use vaderSentiment to calculate the sentiment score for each review. Then, Plotly will visualize the range of sentiment score of each brand with a boxplot and render on the Dashboard. It looks like this. <br><br>
+<img src=jp_whisky_boxplot.png>
+<br>
+From the boxplot, we can learn that reviewers in general have a positive view on the Japanese whiskies, while they have better impression on Nikka and Hibiki. Interestingly, the median sentiment score on Yamazaki is 0, which means neutral.
+
+### TF-IDF Analysis
+The second task is to build a model that shows the summary by displaying the top 5 key words in the review. The script uses TfidfVectorizer from sklearn.feature_extraction.text to build the model. To preprocess the texts, I used the same package to remove English stop words and nltk to stem the words.
+<br>
+<a href="jpwhisky_review_tfidf.py">jpwhisky_review_tfidf.py</a> is the backend, and the Dashboard <a href="">viz.py</a> will provoke the implementation and display result.
+
+## Files
+Here are the files to run the dashboards:
+
+### viz.py
+The driver file to construct the dashboard and backend.
+
+### jpwhisky_review_sentiment.py
+The helper script to calculate sentiment scores in the backend.
+
+### jpwhisky_reivew_tfidf.py
+The helper script to calculate TF-IDF scores in the backend.
+
+### viz_helper Folder
+The framework to render a Plotly visualization, the blueprint comes from the <a href="https://github.com/jacquessham/DashExamples/tree/master/PlotlyTemplateFramework">DashExamples Respository</a> with some modification.

Archive/v2_0_0/jpwhisky_review_sentiment.py

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+import pandas as pd
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+
+# Calculate sentiment score on one element
+def get_score(x):
+	judge = SentimentIntensityAnalyzer()
+	return judge.polarity_scores(x)['compound']
+
+# Calculate sentiment score on a pandas dataframe
+def calculate_sentiment_scores(df):
+	df['score'] = df.apply(lambda x: get_score(x['Review_Content']), axis=1)
+	return df

Archive/v2_0_0/jpwhisky_review_tfidf.py

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+import re
+import string
+import pandas as pd
+import numpy as np
+from nltk.stem.porter import *
+from sklearn.feature_extraction import _stop_words
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+# Preprocess the comment to remove punctuation and split into a list
+def clean_words(post):
+    regex = re.compile('[' + re.escape(string.punctuation) + ' 0-9\\r\\t\\n]')
+    sentence = regex.sub(' ', post.lower())
+    return sentence.split(' ')
+
+# Define tokenizer for Tfidf model
+def tokenizer(post):
+    temp_list = clean_words(post)
+    stemmer = PorterStemmer()
+    temp_list = [stemmer.stem(word) for word in temp_list if 
+                 word != '' and word not in _stop_words.ENGLISH_STOP_WORDS]
+    return temp_list
+
+# Read file and extract the comments and convert to np array
+def read_reviews(filename, colname):
+    jp_whisky = pd.read_csv(filename, encoding = 'ISO-8859-1')
+    comments_list = jp_whisky[colname].tolist()
+    return jp_whisky, comments_list
+
+# Declare model
+def tfidf_fit_trans(comments_list):
+    tfidf = TfidfVectorizer(input='content',
+                            analyzer='word',
+                            tokenizer=tokenizer,
+                            stop_words='english',
+                            decode_error='ignore')
+    # Fit and transform the model
+    scorer = tfidf.fit(comments_list)
+    result = scorer.transform(comments_list)
+    # Get the top 5 scores from each comment
+    features = np.array(scorer.get_feature_names())
+    scores = result.toarray()
+    return features, scores
+
+def get_results(features, scores, article_index, top_n):
+    index = np.argsort(scores[article_index])[::-1]
+    return features[index[:top_n]], scores[article_index, index[:top_n]]
+
+# Design for translating 1 stemword to 1 word, not a dictionary of all words
+# This is not working too well
+def get_words(comment):
+    temp_list = clean_words(comment)
+    stemmer = PorterStemmer()
+    word_dict = {}
+    for word in temp_list:
+        word_stemmed = stemmer.stem(word)
+        if word != '' and word not in _stop_words.ENGLISH_STOP_WORDS \
+           and word_stemmed not in word_dict:
+           word_dict[word_stemmed] = [word]
+        elif word != '' and word not in _stop_words.ENGLISH_STOP_WORDS \
+           and word_stemmed in word_dict:
+           word_dict[word_stemmed] = word_dict[word_stemmed].append(word)
+    return word_dict
+

Archive/v2_0_0/viz.py

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+import json
+from random import choice
+import pandas as pd
+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+from dash.dependencies import Input, Output, State
+from jpwhisky_review_tfidf import *
+from jpwhisky_review_sentiment import *
+from viz_helper.generate_plotly import *
+from viz_helper.table import *
+
+
+##### Data and Predictions #####
+# This is hardcode because the dataset is always static
+filename = 'japanese_whisky_review.csv'
+colname = 'Review_Content'
+whiskies = ['Hibiki', 'Yamazaki', 'Hakushu', 'Nikka']
+df, comments_list = read_reviews(filename, colname)
+df.columns = ['index','Bottle_name','Brand','Title','Review_Content']
+df['index'] = df['index'].astype(int)
+
+# Calculate Sentiment Scores
+df = calculate_sentiment_scores(df)
+
+# Calculate TFIDF scores
+features, scores = tfidf_fit_trans(comments_list)
+
+# Prepare Boxplot for sentiment scores
+with open('viz_helper/arguements.json') as f:
+    args = json.load(f)
+fig = generate_plotly_viz(
+    df, args['metadata'], args['viz_type'], args['viz_name'])
+
+##### Dashboard layout #####
+# Dash Set up
+app = dash.Dash()
+
+# Base Layout
+app.layout = html.Div([
+    html.Div([html.H1('Japanese Whisky Review Analysis')],
+             style={'width': '90%', 'margin': 'auto', 'text-align': 'center'}
+             ),  # Headline
+    html.Div(
+        dcc.Graph(
+            id='sentiment_boxplot',
+            figure=fig
+        )
+    ),  # BoxPlot
+    html.Div([html.H3('Please choose from the following whiskies:')],
+             style={'width': '90%', 'margin': 'auto', 'text-align': 'center'}
+             ),
+    dcc.Tabs(id='whisky-tabs', value=whiskies[0], children=[
+        dcc.Tab(
+                label=whiskies[0],
+                value=whiskies[0]
+        ),  # Tab 1, End whisky1-tab
+        dcc.Tab(
+            label=whiskies[1],
+            value=whiskies[1]
+        ),  # Tab 2, End whisky2-tab
+        dcc.Tab(
+            label=whiskies[2],
+            value=whiskies[2]
+        ),  # Tab 3, End whisky3-tab
+        dcc.Tab(
+            label=whiskies[3],
+            value=whiskies[3]
+        )  # Tab 4, End whisky4-tab
+    ]),  # End Tabs
+    html.Div(id='whisky-table')
+])  # End base Div
+
+##### Dashboard Callback Function #####
+# For Tab1 Only for now
+@app.callback(Output('whisky-table','children'),
+    [Input('whisky-tabs','value')])
+def render_table(tab):
+    # Filter to the brand
+    df_temp = df[df['Brand'] == tab]
+    article_index = choice(df_temp['index'].tolist())
+    top_n = 5
+    # Obtain scores for a randomly selected comment
+    result_features, result_scores = get_results(features, scores,
+                                             article_index, top_n)
+
+    sub_headline = 'This post is about ' + \
+                df_temp[df_temp['index']==article_index].iloc[0]['Brand'] + "'s " + \
+                df_temp[df_temp['index']==article_index].iloc[0]['Bottle_name']
+
+    # sub_headline = 'Hello world!'
+    content = html.Div([
+        html.H3(sub_headline),
+        generate_table_html(result_features, result_scores)
+        ],
+        style={'width': '90%', 'margin': 'auto', 'text-align': 'center'})
+    return content
+
+
+if __name__ == '__main__':
+    app.run_server(debug=True, host='0.0.0.0', port=9000)

Archive/v2_0_0/viz_helper/arguements.json

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+{
+	"df_directory":"../japanese_whisky_review.csv",
+	"viz_type":"boxplot",
+	"viz_name":"Review Sentiment Score Distribution by Whisky",
+	"metadata":{
+		"x":"Brand", 
+		"y":"score"
+	}
+}

Archive/v2_0_0/viz_helper/check_metadata.py

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+# Universal Viz Arg
+def check_text(metadata):
+	if 'text' in metadata:
+		return metadata['text']
+	return None
+
+# Universal Viz Arg
+def check_textposition(metadata):
+	if 'textposition' in metadata:
+		return metadata['textposition']
+	elif 'text_position' in metadata:
+		return metadata['text_position']
+	return None
+
+# Universal Viz Arg
+def check_textfont(metadata):
+	if 'textfont' in metadata:
+		return metadata['textfont']
+	if 'text_font' in metadata:
+		return metadata['text_font']
+	return None
+
+# Bar, Boxplot Arg
+def check_barcolour(metadata):
+	# Allow user to spell English and American English and use of _
+	if 'barcolour' in metadata:
+		return metadata['barcolour']
+	elif 'bar_colour' in metadata:
+		return metadata['bar_colour']
+	elif 'barcolor' in metadata:
+		return metadata['barcolor']	
+	elif 'bar_color' in metadata:
+		return metadata['bar_color']	
+	return None
+
+# Boxplot Arg
+def check_boxmean(metadata):
+	if 'boxmean' in metadata:
+		return metadata['boxmean']
+	return None