Amazon cell phone review sentiment analysis

Table of contents

1. Introduction
2. What is NLP
3. Project Overview
4. Requirements
5. Model
6. Demo
7. Tools Used

Introduction

This is a NLP project with a objective to understand the sentiment behind the cell phone review on amazon.

What is NLP ?

Natural language processing (NLP) is a method to translate between computer and human languages. It is a method of getting a computer to understandably read a line of text without the computer being fed some sort of clue or calculation. In other words, NLP automates the translation process between computers and humans. source

Overview

This project is sentiment analysis project. As the name suggests, sentiment analysis is used to identify the sentiments among several cell phone reviews on amazon. It is also used to identify the sentiment where the emotions are not expressed explicitly. Companies are using sentiment analysis, an application of natural language processing (NLP) to identify the opinion and sentiment of their customers online. It will help companies to understand what their customers think about the products and services. Companies can judge their overall reputation from customer posts with the help of sentiment analysis. In this way, we can say that beyond determining simple polarity, sentiment analysis understands sentiments in context to help us better understand what is behind the expressed opinion.

requirements

Project runs on Python 3.6.1
and all requiremets can be installed by following command you can find requirement.txt here

installing requried modules

pip install -r requirements.txt

downloading nltk

import nltk
nltk.download()

Model

Optimizer

adm = tf.keras.optimizers.Adam(lr=1e-6,decay=1e-3)

Tensorflow early stop callback

loss_moniter = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=1, mode='auto',baseline=None, restore_best_weights=False)

Model summary

Accuracy VS epochs

Loss VS epochs

ROC

Confusion matrix

model can be downloaded from here model.h5

Demo

Input

Output

Example

to run the app clone the repo and run app.py

Tools used

Credits

Data was downloaded from Kaggle