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+# 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>
+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)