Skip to content

Latest commit

 

History

History
132 lines (75 loc) · 3.83 KB

slides.md

File metadata and controls

132 lines (75 loc) · 3.83 KB

Chester Data Insights

Meet up usually at Cornerhouse Bar in Chester, England

Moved online due to CV

Welcome to join the meet up

A friendly meet up with speakers invited to give a talk on a wide range of Data related topics ranging from

  • Open Data
  • Self-service analysis,
  • Business Intelligence,
  • Data integration,
  • AI/Data Science

People across society with an interest in data are welcome.

Very much look forward to seeing you

Subject : Natural Language Processing with Python

Speaker : Lee Hawthorn - recently joined Manchester University.

Goal/Reason for this meetup - demystify data analysis

Thanks to sponsors:

Shortlist Recruiters

With a background in IT management and technical leadership, the directors of ShortList Recruitment know what good IT skills really look like

Intilery

Intilery is the leading real-time Customer Data and CRM platform. Receiving 100's of thousands of data points per second, transforming these into meaningful customer data.

Natural Language Processing

Natural Language Processing (NLP) is the technology used to help machines to understand and learn text and language.

With NLP we aim to teach machines to understand what is said and written to make sense of the human language. It is used to apply algorithms to text and speech to extract

Massive subject area - this is an introduction

Pre-requsitives

Basic knowledge of python

Concepts

Syntactic Analysis

  • Parsing — Involves undertaking grammatical analysis for the provided sentence.

  • Lemmatisation — reduces various inflected forms of a word into a single form. Returns the lemma for the word based on it's intended meaning. Openly researched.

  • Stemming — Cuts the inflected words to their root form. Very fast but not very accurate.

  • Word segmentation — divides a large piece of continuous text into distinct units.

  • Morphological segmentation — divides words into individual units.

  • Part-of-speech tagging — identify the part of speech for every word.

  • Sentence breaking — Places sentence boundaries on a large piece of text

Semantic Analysis

Semantics refers to the meaning of the sentence. A sentence that is syntactically correct does not mean lto be always semantically correct.

Examples

Type Sentence Result
Lower case I Love Python i love python
Punctuation Where is Guido ? Where is Guido
Stop words This is an introduction to NLTK "Introduction NTLK"
Tokenisation This brand is great ['This','brand','is','great']
Stemming I like travelling I like travel
Lemmatisation This coffee filter is better than instant This coffee filter is good than instant"

NLTK

Natural Language Toolkit (NLTK) is an open-source package in Python which allows us to run all common NLP tasks.

It provides easy-to-use interfaces and a suite of text processing libraries with steps involved during preprocessing i.e. classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

Use case : Sentiment Analysis

Lee wants to analyse the sentiment of his blog.

Data pipeline steps

  • Posts on leehbi.com/blog
  • Extract paragraphs
  • Load to Dataframe
  • Clean Data
  • Prepare Data
  • Extract Sentiment
  • Summarise results : Worldcloud, Averages

Other use cases

  • Creditworthiness assessment
  • Neural machine translation
  • Chatbots
  • Sentiment analysis
  • Market intelligence
  • Survey Analysis
  • Clinical Translation
  • Customer feedback analysis

Taking it further