Conversational text Analysis using various NLP techniques

Last update: Dec 25, 2022

Overview

PyConverse

Let me try first

Installation

pip install pyconverse

Usage

Please try this notebook that demos the core functionalities: basic usage notebook

Introduction

Conversation analytics plays an increasingly important role in shaping great customer experiences across various industries like finance/contact centres etc... primarily to gain a deeper understanding of the customers and to better serve their needs. This library, PyConverse is an attempt to provide tools & methods which can be used to gain an understanding of the conversations from multiple perspectives using various NLP techniques.

Why PyConverse?

I have been doing what can be called conversational text NLP with primarily contact centre data from various domains like Financial services, Banking, Insurance etc for the past year or so, and I have not come across any interesting open-source tools that can help in understanding conversational texts as such I decided to create this library that can provide various tools and methods to analyse calls and help answer important questions/compute important metrics that usually people want to find from conversations, in contact centre data analysis settings.

Where can I use PyConverse?

The primary use case is geared towards contact centre call analytics, but most of the tools that Converse provides can be used elsewhere as well.

There’s a lot of insights hidden in every single call that happens, Converse enables you to extract those insights and compute various kinds of KPIs from the point of Operational Efficiency, Agent Effectiveness & monitoring Customer Experience etc.

If you are looking to answer questions like these:-

What was the overall sentiment of the conversation that was exhibited by the speakers?
Was there periods of dead air(silence periods) between the agents and customer? if so how much?
Was the agent empathetic towards the customer?
What was the average agent response time/average hold time?
What was being said on calls?

and more... pyconverse might be of small help.

What can PyConverse do?

At the moment pyconverse can do a few things that broadly fall into these categories:-

Emotion identification
Empathetic statement identification
Call Segmentation
Topic identification from call segments
Compute various types of Speaker attributes:
1. linguistic attributes like: word counts/number of words per utterance/negations etc.
2. Identify periods of silence & interruptions.
3. Question identification
4. Backchannel identification
Assess the overall nature of the speaker via linguistic attributes and tell if the Speaker is:
1. Talkative, verbally fluent
2. Informal/Personal/social
3. Goal-oriented or Forward/future-looking/focused on past
4. Identify inhibitions

What Next?

Improve documentation.
Add more use case notebooks/examples.
Improve some of the functionalities and make it more streamlined.

Built with:

Transformers	Spacy	Pytorch

Credits:

Note: The backchannel Utterance classification method is inspired by facebook's Unsupervised Topic Segmentation of Meetings with BERT Embeddings paper (arXiv:2106.12978 [cs.LG])

It is a system used to detect bone fractures. using techniques deep learning and image processing

MohammedHussiengadalla-Intelligent-Classification-System-for-Bone-Fractures It is a system used to detect bone fractures. using techniques deep learni

7 Nov 11, 2022

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

8 Nov 1, 2022

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab

398 Dec 30, 2022

Collection of NLP model explanations and accompanying analysis tools

Thermostat is a large collection of NLP model explanations and accompanying analysis tools. Combines explainability methods from the captum library wi

126 Nov 22, 2022

A Python multilingual toolkit for Sentiment Analysis and Social NLP tasks

pysentimiento: A Python toolkit for Sentiment Analysis and Social NLP tasks A Transformer-based library for SocialNLP classification tasks. Currently

298 Jan 7, 2023

Library of various Few-Shot Learning frameworks for text classification

FewShotText This repository contains code for the paper A Neural Few-Shot Text Classification Reality Check Environment setup # Create environment pyt

47 Jan 3, 2023

🐦 Opytimizer is a Python library consisting of meta-heuristic optimization techniques.

Opytimizer: A Nature-Inspired Python Optimizer Welcome to Opytimizer. Did you ever reach a bottleneck in your computational experiments? Are you tired

546 Dec 31, 2022

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin

8.1k Jan 6, 2023

tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

2.8k Jan 8, 2023

Comments

SemanticTextSegmentation NaN With All Stop Words

When running semantic text segmentation, I found that if the input utterance line is all stop words, (i.e. "Bye. Uh huh. Yeah."), SemanticTextSegmentation._get_similarity fails with ValueError: Input contains NaN.

I found that adding a check for nan in both embeddings could solve this problem.

def _get_similarity(self, text1, text2):
    sentence_1 = [i.text.strip()
                  for i in nlp(text1).sents if len(i.text.split(' ')) > 1]
    sentence_2 = [i.text.strip()
                  for i in nlp(text2).sents if len(i.text.split(' ')) > 2]
    embeding_1 = model.encode(sentence_1)
    embeding_2 = model.encode(sentence_2)
    embeding_1 = np.mean(embeding_1, axis=0).reshape(1, -1)
    embeding_2 = np.mean(embeding_2, axis=0).reshape(1, -1)

    if np.any(np.isnan(embeding_1)) or np.any(np.isnan(embeding_2)):
            return 1

    sim = cosine_similarity(embeding_1, embeding_2)
    return sim

I would like to have someone else look at it because I don't want to make any assumptions that the stop words should be part of the same segments.

opened by Haowjy 1

Updated lru_cache decorator.

After installing and running the library pyconverse on python-3.7 or below and using the import statement it gives error in import itself. I went through the utils file and saw that the "@lru_cache" decorator was written as per the new python(i.e. 3.8+) style hence when calling in older versions(py 3.7 and below it raises a NoneType Error) as the LRU_CACHE decorator is written as -" @lru_cache() " with paranthesis for older versions . Hence made the changes. The changes made do not cause any error on the newer versions.

opened by AkashKhamkar 0
Error in importing Callyzer, SpeakerStats

When I want to load the model it's showing this error.Whether it is currently in devloped mode

KeyError: "[E002] Can't find factory for 'tok2vec'. This usually happens when spaCy callsnlp.create_pipewith a component name that's not built in - for example, when constructing the pipeline from a model's meta.json. If you're using a custom component, you can write to Language.factories['tok2vec'] or remove it from the ### model meta and add it vianlp.add_pipeinstead.

opened by kalpa277 0

Releases(v0.2.0)

v0.2.0(Nov 21, 2021)
First Release of PyConverse library.

Conversational Transcript Analysis using various NLP techniques.

Emotion identification

Empathetic statement identification

Call Segmentation

Topic identification from call segments

Compute various types of Speaker attributes:

linguistic attributes like : word counts/number of words per utterance/negations etc

Identify periods of silence & interruptions.

Question identification

Backchannel identification

Assess the overall nature of the speaker via linguistic attributes and tell if the Speaker is:

Talkative, verbally fluent

Informal/Personal/social

Goal-oriented or Forward/future-looking/focused on past

Identify inhibitions

Source code(tar.gz)
Source code(zip)

Owner

Rita Anjana

ML engineer

GitHub Repository

PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

516 Jan 05, 2023

3.8% and 18.3% on CIFAR-10 and CIFAR-100

Wide Residual Networks This code was used for experiments with Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko

1.2k Dec 29, 2022

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains a PyTorch implementation for the paper Score-Based Genera

757 Jan 04, 2023

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation This repository contains PyTorch evaluation code, trainin

45 Dec 20, 2022

Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields"

NeRF++ Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields" Work with 360 capture of large-scale unbounded scenes. Sup

722 Dec 28, 2022

Meshed-Memory Transformer for Image Captioning. CVPR 2020

M²: Meshed-Memory Transformer This repository contains the reference code for the paper Meshed-Memory Transformer for Image Captioning (CVPR 2020). Pl

422 Dec 28, 2022

An Implementation of Fully Convolutional Networks in Tensorflow.

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

1.1k Dec 12, 2022

Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Fast mesh denoising with data driven normal filtering using deep variational autoencoders This is an implementation for the paper entitled "Fast mesh

9 Dec 02, 2022

The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

SF-Net for fullband SE This is the repo of the manuscript "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Ban

36 Dec 02, 2022

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022