Conversational text Analysis using various NLP techniques

Related tags

Text Data & NLPconverse
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:-

  1. What was the overall sentiment of the conversation that was exhibited by the speakers?
  2. Was there periods of dead air(silence periods) between the agents and customer? if so how much?
  3. Was the agent empathetic towards the customer?
  4. What was the average agent response time/average hold time?
  5. 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:-

  1. Emotion identification
  2. Empathetic statement identification
  3. Call Segmentation
  4. Topic identification from call segments
  5. 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
  6. 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?

  1. Improve documentation.
  2. Add more use case notebooks/examples.
  3. 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])

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Comments
  • SemanticTextSegmentation NaN With All Stop Words

    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.

    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

    Error in importing Callyzer, SpeakerStats

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

    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.

    1. Emotion identification
    2. Empathetic statement identification
    3. Call Segmentation
    4. Topic identification from call segments
    5. 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
    6. 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
Rita Anjana
GitHub Repository
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