#2 Twitter Sentiment Analysis Using Python | Machine Learning | Mini Projects | The Easy Concepts

Опубликовано: 01 Март 2025
на канале: The Easy Concepts

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#sentimentanalysis #machinelearning #projectideas

Twitter Sentiment Analysis | Opinion Mining Using Python for Computer Science and Engineering Students | Machine Learning | Neural Network | Mini Projects | The Easy Concepts

In this video, we have discussed sentiment analysis or opinion mining project.
Our motive here is to provide you something that can help you in enhancing your skills, making you industry ready, preparing you for interviews, and most importantly helping you to build your interest in computer science – So let’s begin with it and talk about an exciting project idea along with all its technical details that are necessary for you to begin with and end the project successfully.
In the previous video, we talked about 5 projects, which you can watch here -    • #1 Five Best Project Ideas for Comput...

1. Tik Tac Toe
2. Weather App
3. BlackJack game
4. Chat Application
5. Web Scraping

Sentiment analysis (also known as opinion mining is a text analysis technique that detects polarity (e.g. a positive or negative opinion) within a text, within a whole document, paragraph, sentence, or clause.

You need two things for this mini-project -
1. You should have a Twitter developer account
2. You should have a basic understanding of Python programming

The Twitter developer portal is a set of self-serve tools that developers can use to manage their access as well as to create and manage their Projects and Apps.

Sometimes there is a lot of raw data so it’s challenging to understand JSON data – for that you can use this website - http://jsonviewer.stack.hu/

First, you need to preprocess the downloaded data which means you need to remove all unnecessary things from the tweets that are not required for the sentiment analysis task. This phase is very critical and a crucial step in data mining as it hugely impacts the analysis. There are high chances that unprocessed data remains inconsistent and noisy which might result in incorrect analysis. To make the data uniform and clean we preprocess it by removing all the characters, words, and phrases that are less significant or carry less weightage in sentiment analysis.

you can use a Lexicon or ML algorithm or Neural Network -
1. If you use a lexicon like NRC, AFFIN, or WordNet, you need to use an already built list of words called a dictionary or lexicon, that contains words and their sentiments.
2. In case you want to use ML algorithms or Neural network, you need some already tagged data with polarities or you need to tag the data on your own on which you can train your model and find out the sentiments using suitable algorithms.

You can visit the Google Scholar profile of Deepak Uniyal - https://scholar.google.com/citations?...

and read about a similar task that has been done using the NRC lexicon and machine learning algorithms.

Please watch the full video to learn the concepts in more detail.
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