This document discusses techniques for rating cooking recipes based on user reviews, including sentiment analysis and text mining. Sentiment analysis involves classifying the polarity of reviews as positive or negative using techniques like preprocessing text, detecting polarity words, and calculating sentiment scores. Text mining is used to extract and analyze useful information from the large amounts of unstructured text data about recipes from sources like reviews. The analysis of reviews using these techniques helps users find the best recipes and provides feedback to recipe authors.

1. COOKING RECIPE RATING
Prepared By - Sneha Nandanwar
Guided by –
Pranay Patel , Asst. Prof. CP Dept.
Birla Vishvakarma Mahavidyalaya
Seminar (CP602 )

2. CONTENTS
 Introduction
 Techniques
 Sentimental analysis
 Text mining
 Conclusion

3. WHERE WORDS FAIL,
FOOD SPEAKS.

4. INTRODUCTION
 In order to get right recipes, recipes must be
rated by the user.
 Recipes are rated and commented by the visitors.
 This saves time of the users searching for the
best recipe.
 Review that has the highest score was ranked at
 first position and so on.

5. TECHNIQUES
Text
summarization
Sentiment
analysis
TECHNIQUES

6. SENTIMENT ANALYSIS
 Sentiment analysis, or so called opinion mining.
 It involves natural language processing, text analytics
and computational linguistics.
Basic objective
Extract useful information Classify the polarity

7. ANALYSIS PROCESS
 Preprocessing
 Detecting Polarity Words
 Calculating Polarity Scores

8.  Sentiment Analysis is challenging, as it doesn’t work well
with basic lexical-based classification.
 For example: suppose someone has posted a review about a
recipe say “Mexican Pizza”.

9.  Sentiment Analysis is classified as:-
a. Machine learning approach
b. Lexicon- based approach
 In accordance to Latent Aspect Rating Analysis [LARA]
Model, the words from reviews are given some weights.
 For example:
“I loved the recipe. Tomato in the recipe made it
more delicious.”

10. TEXT MINING
 Text data is everywhere – books ,news ,blogs , social
networking , etc.
 According to estimate,80% of world’s data is in unstructured
text format.
 We need to extract, summarize and analyze useful information
from unstructured/text data.
 So text mining can be used .

11. Remove Stop
words
Bag of Words
Stemming or
Lemmatization
Tokenization
TECHNIQUES

12. CONCLUSION
 This saves time of the users searching for the best recipe
for a particular ingredient.
 A bag of positive and negative words like “delicious”,
“bad” etc. were used to rate the reviews.
 The recipe authors can gain information that how many
peoples like or dislike the recipes.

13. THANK YOU……
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