Cooking Recipe Rating Based On Sentiment Analysis

A recipe website that rates recipes from user reviews. Users post recipes by category, and sentiment analysis on comments using admin-defined keywords gives each recipe a rating so visitors find the best ones.

Text is collected from the source (reviews, posts, messages or documents). It is cleaned: lowercased, stop words removed, tokenised and lemmatised. The text is converted to features with TF-IDF or embeddings and a classifier or language model processes it. Results such as sentiment, category or a reply are shown on a dashboard or chat interface.

Lowercasing, removing punctuation and stop words, tokenisation and stemming or lemmatisation. The cleaned text is then converted to numbers with bag-of-words, TF-IDF or embeddings.

Term frequency–inverse document frequency gives a word a high weight if it appears often in one document but rarely across all documents, so common words like 'the' get low importance.

It finds frequent itemsets in transaction data by building larger itemsets only from smaller ones that are already frequent, and then derives association rules with support and confidence above set thresholds.

It follows a three-tier architecture: the browser shows the user interface, the server-side code handles logic and validation, and the MySQL database stores records. Requests travel over HTTP and responses are rendered as HTML or JSON.

All queries use prepared statements with bound parameters, so user input is never joined into the SQL text. Inputs are also validated on the server.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.