Sentiment Analysis ML Flask Python Web App Project with Source Code

A machine learning end to end flask web app for sentiment analysis model created using Scikit-learn & VADER Sentiment.

The project uses libraries like :

1. Flask
2. Sklearn
3. Requests
4. NLTK
5. RE
6. vaderSentiment

VADER SENTIMENT

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.
More about VADER

ABOUT SENTIMENT ANALYSIS

Sentiment analysis, an important area in Natural Language Processing, is the process of automatically detecting affective states of text. Sentiment analysis is widely applied to voice-of-customer materials such as product reviews in online shopping websites like Amazon, movie reviews or social media. It can be just a basic task of classifying the polarity of a text as being positive/negative or it can go beyond polarity, looking at sentiment states etc.
Sentiment analysis refers to analyzing an opinion or feelings about something using data like text or images, regarding almost anything. Sentiment analysis helps companies in their decision-making process. For instance, if public sentiment towards a product is not so good, a company may try to modify the product or stop the production altogether in order to avoid any losses.

There are many sources of public sentiment e.g. public interviews, opinion polls, surveys, etc. However, with more and more people joining social media platforms, websites like Facebook and Twitter can be parsed for public sentiment.

Supported Operating System

• We can configure this project on following operating system.
• Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
• Python 3.7, PIP, Flask.
• Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Steps :-

• Install Python 3.7 Or Higher
• Install all dependencies cmd -python -m pip install --user -r requirements.txt
• Finally run cmd - python app.py

Download Source Code