Natural Language Processing for Products

In all communications, even between family, some degree of noise exists. The product marketer’s goal is communicating value in a manner that resonates and builds confidence with consumers. Each customer is different and their reception of a message is, to a large degree, dependent on their unique behavioral and social qualities.

Each user interaction with a product page is an opportunity to develop a customer relationship. Each loyal customer is an opportunity for post-conversion engagement, product reviews, and user-generated brand content via social media. Human writers are limited to learning about user segment responses and find it more challenging to analyze which aspects of a product description is generating various aspects of user engagement and message response.

Advanced Natural Language Processing (NLP) Tools
Today’s Natural Language Processing (NLP) applications are capable of processing every word ever published on the internet, including vocal recordings, video voiceover, and image-imbedded words. Natural language processing technologies enable us to drill down into these words, sifting through language by subject, topic, and context. For example, Writesof can analyze every word of text on product pages and social media describing red sweaters. Each message will have different ways of describing features, benefits, uses, and advantages of red sweaters. By analyzing all of this language, we can dissect, for example, every adjective that describes the product attribute, “red”, as well as how each adjective is used in context with the other language.

Language, generally speaking, is comprised of a relatively small set of words, word structures, and grammar rules. In fact, every word currently published on the internet bears some measurable relativeity to every other word ever written. As dimensions of language are narrowed, for example, by scope, relationships within the language become closer to each other. Narratives that mention horses are going to be vastly different, in linguistic properties, than narratives about red sweaters, but there’s a pretty good chance that someone has written something that mentions both horses and red sweaters in a single document. This can be viewed as a type of statistical mapping of languages. The word, “cat” has a measurable relationship with the word, “beach”. While there aren’t many written references of cats on beaches, there is still some measurable degree of correlation between these two very different words. The spacial relationship between words can often, but not always, narrow when you introduce a new word in the contextual space, for example, adding the word “hotel” to the word map, could retrieve context frames such as, “Just took our cat to the pet hotel. We’ll see you on the beach tomorrow.” As a human, the neural network in your brain can process hundreds of context relationships between the words “cat”, “hotel” and “beach”. Natural language processing can work in a similar manner, using recurrent neural networks and probabilistic language modeling to classify exactly how all types of language is used, with frequency of usage as the primary source of validation. Validating the word, “the”, before the word, “go”, for example would score very low on usage frequency, but would return classifiable metrics in context with some slang usage, as well as named entities, for example, the early 80’s pop group, “The Go-Go’s”

linguistics
language as a phenomenon
artificial inteligence
subfields
non-Enlgish NLP
deep learning
word embeddings
open-source
NLP toolkits
noisy data
unstructured data
textual data
entity extraction
document classification
topic modeling
natural language understanding
producing
evaluating
training data
computational linguistics
related fileds
NLP methods
information extraction
parsing
relationship extraction
knowledge databases
language ontologies
text pre-processing
text normalization
tokenization
POS tagging
parsing
sentiment analysis
source code management
debugging
testing
depoloyment
research methods
communicating insights
presenting concepts
diverse audience
targeted autdience
personalization
insurance
investments
finance
physics
computer science
statistics
numeric discipline
text analytics
text classification
topic detection
named entity recognition
entity resolution
question-answering
dialog systems
chatbots
event detection
language modeling
hands-on experience
hyper-parameter tuning
deep construction
deep distribution
CNN convolutional neural network
RNN recurrent neural network
LSTM long-short term memory units of a neural network
large scale text mining
multi-domain text corpora
text streams
data streams
junior data scientist
real-time computing
integrated deep learning
mobile apps
front-end systems
product development team
knowledge extraction
knowledge graphs
text summarization
semantic role labeling
information retrieval
crowdsourcing
engineering and design
architecture and design
product teams
graph theory
hierarchical modeling
Bayesian inference
modern big-data computing ecosystems
business stakeholders
BI / AI solutions
dashboard
visualization
extensions (e.g. BI solution)
compelling recommendations
compelling insights
decision makers
descriptive analytics
predictive analytics
prescriptive analytics
complex data analysis
statistical methodologies
machine learning algorithms
AI techniques
productizing of intelligence
operationalization of intelligence
querying
scraping
loading
validating
cleaning
aggregating
transforming
detecting outliers
imputing missing values
developing features
scripting of these processes
reproducable results
reusable components
peer review
NLP applications
NLP modeling
evaluating training data
utilizing training data
word embedding
stemming
lemmatization
sentiment analysis
confidence intervals
relevant statistical measures
key paramaters
affect their performance
machine learning techniques
large amounts of text data
extracting insights
TFIDF
N-gram modeling
document classification
text pre-processing
POS tatting
conversational AI
dialig management
question answering
new metrics
sophisticated understanding
brand’s audience
high-impact role
data solutions
head of strategy
head of research and development
connect better with their audiences
computational methods
creating new tools
provide more value to their audiences
current and future audiences
use of data in
increases its focus
focus on its audience
what’s known about the audience
data scientist
shaping the key metrics
predictive models
current and future audience
segmenting into groups
audience behavior
A/B testing
content reach
basic copywriting workflow
content types
what has worked well
data dashboards
marketing tools
product information management
product data tools
digital asset management
common product types
audience data sets
working knowledge
effective metrics
to inform decision making
data engineer
untapped audience data sets
recommendation engines
template writing
applying computational methods

Address