Lets load a set of messages along with appropriate classification using following command.
messages <- read.table( file.choose(), sep="\t", stringsAsFactors=FALSE)
The messages data frame could have two features, such as type and text where each piece of text is associated with an appropriate type. Once done, lets go ahead and create a Corpus object out of all the message text. Following command helps to create a Corpus object. For those of you who are new to Corpus object, note that it comes as part of famous text mining R package named as “tm”.
corpus <- Corpus(VectorSource(messages$text))
Once Corpus object is created it is time to clean the text.
Following is cleaned as part of text cleaning activity:
Following is command set that achieves above objectives:
# Change all the words to lowercase
corpus_clean <- tm_map(corpus, content_transformer(tolower))
# Remove all the numbers
corpus_clean <- tm_map(corpus_clean, removeNumbers)
# Remove the stop words such as to, and, or etc.
corpus_clean <- tm_map(corpus_clean, removeWords, stopwords())
# Remove punctuation
corpus_clean <- tm_map(corpus_clean, removePunctuation)
# Remove whitespaces
corpus_clean <- tm_map(corpus_clean, stripWhitespace)
Large language models (LLMs) have fundamentally transformed our digital landscape, powering everything from chatbots and…
As Large Language Models (LLMs) evolve into autonomous agents, understanding agentic workflow design patterns has…
In today's data-driven business landscape, organizations are constantly seeking ways to harness the power of…
In this blog, you would get to know the essential mathematical topics you need to…
This blog represents a list of questions you can ask when thinking like a product…
AI agents are autonomous systems combining three core components: a reasoning engine (powered by LLM),…