This repository provides solution of the team, noobs451 for the Competition, Hack-the-Talk. This team placed eight in the competition. https://www.hackerearth.com/sprints/hack-the-talk/

Acoustic Sentimental Analysis: Our project uses an open library pyAudioAnalysis for feature Extraction. The features extracted are:

1. Zero Crossing Rate
2. Energy
3. Entropy of Energy
4. Spectral Centroid
5. Spectral Spread
6. Spectral Entropy
7. Spectral Flux
8. Spectral rolloff 9-21. MFCC 22-33. Chroma Vector
9. Chroma Deviation

We explcitly convert the .mp3 files to .wav files using pydub.

The trained data set is loaded in a pickle file so as to avoid training everytime.

The Classifier used for prediction is Random Forest Classifier with No of estimators: 30 No of Features: 6

Operating system Ubuntu-14.04 and above Python 2.7 and above

1. execute.py: python program for training and predicting the sentiment of files from input.txt
2. setup.py: python program that installs required packages and libraries to run the project.
3. run.sh: executes setup.py and execute.py
4. dataset.pickle , classifier.pickle: pickle file with trained dataset and classifier set.

1. Extract our zip file.
2. In order to be able to call the pyAudioAnalysis library from any path you need to add the folder that contains it in the ~/.bashrc file. In particular, add a line as the follow in ~/.bashrc:

export PYTHONPATH=$PYTHONPATH:"/home/bla/bla"

(use the exact path where the pyAudioAnalysis folder is contained - without the pyAudioAnalysis name, e.g. if the library is contained in /home/tyiannak/Research/libraries/pyAudioAnalysis, then use /home/tyiannak/Research/libraries in the bashrc file)

Then, you need to update the path details:

source ~/.bashrc

3. Add the files to be predicted in given folder and their name in input.txt.
4. Execute " bash run.sh "

If you wish to change the training dataset just uncomment the line 'train_classifier()' in main in execute.py. This will create new classifier and save it in classifier.pickle.