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.
positive, neutral, or negative
feelings from text.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.
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. |
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
fork
the repository - Creates a cope of this project in your github so that you can make changes locally.git clone https://github.com/<GITHUB USERNAME>/Twitter-video-emotion-and-sentiment-analysis.git
.pip install -r requirements.txt
. Use a virtual environment to keep the all dependencies in a separate enviroment for example - conda
, virtualenv
, pipenv
, etc.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 :fork_and_knife: 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.
git clone https://github.com/<GITHUB USERNAME>/Twitter-video-emotion-and-sentiment-analysis.git
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'
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 ;)
Make relevant changes. Add new algorithms, Add Readme files, Contribute in any way you feel like :)
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
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.
You have made your contributions. Kudos to you!! 🎉✌🏻🙌🏻