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An overview of the Project?

The main aim of this project is to perform sentiment and emotion analysis of all the videos uploaded to twitter by tagging the twitter bot which we have created in this project. We can't fetch the videos without mentioning the bot as twitter doesn't allow this, so we need our own Twitter developer API.

What is sentiment analaysis then?

• Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study effective states and subjective information. Sentiment Analysis aims to detect positive, neutral, or negative feelings from text.
• Typically sentiment analysis seems to work best on subjective text, where people express opinions, feelings, and their mood. But, this project is not only going to make sentiment analysis on text, it will work on identifying emotions in speech and videos which makes it a unique idea to implement and contribute. In simpler words, The idea is to analyze and understand the reactions of people toward a specific entity and take insightful actions based on their sentiment.

Now what is emotion analysis?

Emotion analysis is the process of identifying and understanding human emotions. Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, and surprise.

Files used in this project

File	Description
Data	This directory contains further 3 folders namely speech, text, video.
	- speech folder contains a python pickle file named Emotion_Voice_Detection_Model.pkl which is a voice detection model which acts on a speech.
	- text folder contain files with words, misspelled words and contractions for data cleaning, stemming, lemmatization.
	- video folder contains files with video paths and some emotion tags for pre-processing.
keys.py	This file contains keys for authorization purposes for having a convenient connection access between twitter API and our bot.
remove.py	This file contains a remove method to remove the specified file path downloaded while checking emotion analysis for an audio or video tweet as it directly interacts with the Operating System.
requirements.txt	This is a text file containing all libraries , packages with their respective versions.
speech_emotion.py	This is a python file which contains a method to extract features and another to predict the emotions during a speech.
text_emotion.py	This is a python file which contains methods to extract text from speech and handling or text cleaning of the misspelled, contractions, punctuations and emojis, eventually giving the text sentiment.
video_emotion.py	This is a python file which contain methods to run a video, write emotion on excel sheet and calculate the percentage of every emotion in the video.
threads.py	This is a python file which is responsible to store the emotions from speech, text and videos using threads. This file also contain methods to convert and reduce quality of file either it is an audio or a video.
tweet_check.py	This is a python file which is responsible for making an authorized connection between the twitter API and the bot. This file reads and writes the tweets made on twitter.

Requirements and versions

• cmake - 3.20.5 | requests - 2.22.0
• dlib - 19.22.0 | requests-oauthlib - 1.3.0
• emoji - 1.2.0 | requests-unixsocket - 0.2.0
• fastai - 1.0.61 | responses - 0.13.3
• fastcore - 1.3.20 | scikit-learn
• fastprogress - 1.0.0 | scipy
• Flask - 1.1.2 | simplejson - 3.16.0
• imutils - 0.5.4 | SpeechRecognition - 3.8.1
• Keras - 2.4.3 | tensorflow
• Keras-Preprocessing - 1.1.2 | tensorflow-estimator
• librosa - 0.8.1 | tensorflow-gpu
• matplotlib - 3.4.2 | tokenizers - 0.10.3
• matplotlib-inline - 0.1.2 | torch
• moviepy | tweepy
• numpy | tqdm - 4.61.1
• opencv-python | transformers - 4.8.1
• openpyxl - 3.0.7 | tweepy - 3.10.0
• pandas - 1.2.5 | tweet-preprocessor - 0.6.0
• pyforest - 1.1.0 | urllib3
• pydub | websocket-client - 1.1.0

Okay you know everything now, Let's start

Let's setup in your local machine first:

1. fork the repository - Creates a cope of this project in your github so that you can make changes locally.
2. Clone the repository to your local machine using git clone https://github.com/<GITHUB USERNAME>/Twitter-video-emotion-and-sentiment-analysis.git.
3. Now navigate to the dowloaded folder to install the requirements.txt using command pip install -r requirements.txt. Use a virtual environment to keep the all dependencies in a separate enviroment for example - conda, virtualenv, pipenv, etc.
4. Now you can start your coding magic to make some awesome changes.

How to Contribute? Let's Get Started:

Step 1. Create a Copy of this Repository

To work on an open-source project, you will first need to make your copy of the repository. To do this, you should fork the repository and then clone it so that you have a local working copy.

Fork 🍴 this repo. Click on the Fork button at the top right corner.

With the repository forked, you’re ready to clone it so that you have a local working copy of the codebase.

Clone the Repository

To make your local copy of the repository you would like to contribute to, let’s first open up a terminal window.

We’ll use the git clone command along with the URL that points to your fork of the repository.

• Open the Command Prompt or your git bash terminal
• Type this command:

git clone https://github.com/<GITHUB USERNAME>/Twitter-video-emotion-and-sentiment-analysis.git

Step 2: Creating a New Branch

It is important to branch the repository so that you can manage the workflow, isolate your code, and control what features make it back to the main branch of the project repository.

When creating a branch, you must create your new branch off of the master branch. To create a new branch, from your terminal window, follow:

git branch new-branch
git checkout new-branch

Once you enter the git checkout command, you will receive the following output:

Switched to branch 'new-branch'

Step 3: Environment setup:

Once You have installed the repositories, make sure to install dependencies using pipenv with the provided Pipfile and execute all commands using pipenv. Also, please make sure to add the correct path to the video file in camera.py on line 11. Next, to install pipenv, the dependencies, and run the main.py file, execute the following commands from your terminal or command prompt, making sure to add the right paths where necessary:

cd \path\to\Project-Exp-Recog\
pip install pipenv
pipenv install
pipenv run python3 main.py

Now Your environment is ready to contribute ;)

Step 4: Contribute:

Make relevant changes. Add new algorithms, Add Readme files, Contribute in any way you feel like :)

Step 5: Commiting and Pushing:

Once you have modified an existing file or added a new file to the project, you can add it to your local repository, which we can do with the git add command.

git add filename or git add .

You can type the command git add -A or alternatively git add -all for all new files to be staged.

With our file staged, we’ll want to record the changes that we made to the repository with the git commit command.

The commit message is an important aspect of your code contribution; it helps the other contributors fully understand the change you have made, why you made it, and how significant it is.

git commit -m "commit message"

At this point you can use the git push command to push the changes to the current branch of your forked repository:

git push --set-upstream origin new-branch

Step 6: Create Pull Request

At this point, you are ready to make a pull request to the original repository.

You should navigate to your forked repository, and press the “Compare & pull request” button on the page.

GitHub will alert you that you can merge the two branches because there is no competing code. You should add in a title, a comment, and then press the “Create pull request” button.

Step 6: CONGRATULATIONS 💥 👏 ☺️

You have made your contributions. Kudos to you!! 🎉✌🏻🙌🏻

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