Merge pull request #1 from jacquessham/v2

Release v2.0

.gitignore

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+#.idea/

Archive/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/jpwhisky_review_sentiment.py

@@ -0,0 +1,19 @@
+import pandas as pd
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+
+def get_score(x):
+	judge = SentimentIntensityAnalyzer()
+	return judge.polarity_scores(x)['compound']
+
+
+# Cols: 'Unnamed: 0','Bottle_name','Brand','Title','Review_Content'
+jp_whisky = pd.read_csv('japanese_whisky_review.csv')
+# Demostrate how to get a score in one row
+score = get_score(jp_whisky.iloc[1,3])
+print(score)
+
+# Calculate score for each row
+jp_whisky['score'] = jp_whisky.apply(lambda x: get_score(x[3]), axis=1)
+print(jp_whisky)
+

Archive/jpwhisky_review_tfidf.py

@@ -0,0 +1,64 @@
+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
+

README.md

@@ -1,32 +1,56 @@
 # 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>
+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.
-<br>
+<br><br>
+<b>The current version is 2.0.</b> You may find the previous versions in the [Archive](/Archive) folder.
 
 ## Tools
-In this project, I will be using packages like SKlearn, vaderSentiment, ntlk for sentiment scores and TF-IDF. <br>
+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.
 
-## 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>
+
+## 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>
 ![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.
+### 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>
-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)
+<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.

jpwhisky_review_sentiment.py

@@ -2,13 +2,12 @@
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 
 
+# Calculate sentiment score on one element
 def get_score(x):
 	judge = SentimentIntensityAnalyzer()
 	return judge.polarity_scores(x)['compound']
 
-
-jp_whisky = pd.read_csv('japanese_whisky_review.csv')
-score = get_score(jp_whisky.iloc[1,3])
-print(score)
-jp_whisky['score'] = jp_whisky.apply(lambda x: get_score(x[3]), axis=1)
-print(jp_whisky)
+# 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