Natural Language Processing and Audio Processing.

Audio is sent to DEEP-LEARNING 7-LAYERED DNN Model , which considers spectrogram of the audio and uses the Librosa library for classifying the particular emotions Angry, Sad, Disgust, Surprised, and a testing accuracy of 75%.

The calls are classified based on following features in our DL Model:

MelSpectrogram, MFCC, Spectral-Contrast, Chroma,Tonnetz

The text is prioritized using TF-IDF, Page Ranking, Cosine Similarity using steps:

Pre-Processing-> TF-IDF Matrix -> Similarity Semantics -> Page ranking.

The tweets are prioritized using Integer Linear Programming methods like Primal and Dual, and using library such as

Textacy, Spacy, NLTK, PyMathProg.

Tweet Summarization is Achieved in 3 steps:

1.I want the total length of all the selected tweets to be less than some value L

2.If I pick some content word (out of my possible content words) , then I want to have at least

one tweet from the set of tweets which contain that content word, .

3.If I pick some tweet i (out of my possible tweets) , then all the content words in that tweet are also selected.