Objective:

To develop a web-based application that analyzes customer reviews of products and determines their sentiment — positive, negative, or neutral — using machine learning and natural language processing techniques. This helps businesses better understand customer opinions and improve their services or products.

Technologies to be Used:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: PHP / Java / Node.js

• Machine Learning (NLP): Python (with libraries like NLTK, Scikit-learn, TextBlob, or TensorFlow/Keras)

• Database: MySQL / MongoDB / PostgreSQL

• Integration: REST API to connect Python ML model with PHP/Java/Node backend

Key Features:

1. User Input Form:

   • Text area for users to enter product reviews.

   • Option to upload a CSV file with multiple reviews.

2. Sentiment Classifier:

   • Backend connects to a trained Python model.

   • Classifies each review into:

     •  Positive

     •  Negative

     •  Neutral

3. Result Display:

   • Display results on the frontend with icons and colors.

   • Show confidence score (e.g., "85% Positive").

4. Dashboard for Admin/Business Users:

   • Visual charts showing the distribution of sentiments.

   • Word cloud of common words in positive/negative reviews.

   • Filter by product category or time range.

5. Data Export:

   • Download sentiment results in Excel or PDF format.

   • API integration for connecting to e-commerce platforms (optional).

Machine Learning Model (Python Side):

• Preprocessing:

  • Remove stop words, punctuations

  • Tokenization

  • Lemmatization

• Vectorization:

  • TF-IDF or CountVectorizer to convert text to numbers

• Algorithms:

  • Naive Bayes or Logistic Regression (Basic)

  • SVM or Random Forest (Intermediate)

  • LSTM, BERT (Advanced)

• Model Output:

  • Sentiment label + probability score