This work consists of evaluating an algorithm that allows the classification of analysis of feelings from the tweets provided by people who have traveled in one of the most important airlines in the United States, giving your opinion regarding the service received by the company’s staff, the importance of this work is to try to minimize the percentage of error at the time of classifying , this work shows that it is able to improve the classification by having as a base a large number of samples.

Consider the problem of classifying quickly and automatically with minimal error the comments received in their Twitter accounts of the different airlines, this represents a major problem for the different companies who want to know the perspective that users have about the service they offer. Through learning algorithms that can be trained to carry out the classification, these tasks can be automated, making it easier for companies to classify the work manually.

For this project we will use the dataset AirlinesTweets that has 14640 observations of the tweets about the comments of the problems of each of the major U.S. airlines in which taxpayers were asked to classify with positive first, negative or neutral, followed by negative or positive reasons (such as “Late Flight” or “rude service”).

The data were collected using the Twitter API using the official accounts of some of the American Airlines an analysis.

In this second step we prepare the data to be transformed for this analysis will use the word bag method, which is to ignore the order of words and only present a variable that indicates if the word appears.

Verify the structure of the dataset.

setwd("C:/Users/luisoftgb28/Desktop/Proyecto2/Proyecto2")
airlines <- read.csv("AirlinesTweets.csv",stringsAsFactors = FALSE)
str(airlines)

## 'data.frame':    14640 obs. of  4 variables:
##  $ tweet_id         : num  5.7e+17 5.7e+17 5.7e+17 5.7e+17 5.7e+17 ...
##  $ airline_sentiment: chr  "neutral" "positive" "neutral" "negative" ...
##  $ airline          : chr  "Virgin America" "Virgin America" "Virgin America" "Virgin America" ...
##  $ text             : chr  "@VirginAmerica What @dhepburn said." "@VirginAmerica plus you've added commercials to the experience... tacky." "@VirginAmerica I didn't today... Must mean I need to take another trip!" "@VirginAmerica it's really aggressive to blast obnoxious \"entertainment\" in your guests' faces & they have little recours"| __truncated__ ...

As you can see we have the field airline_sentiment of type character, in order to perform some functions we will convert this field to a factor.

We transform the field to factor airline_sentiment.

airlines$airline_sentiment <- factor(airlines$airline_sentiment)
str(airlines$airline_sentiment)

##  Factor w/ 3 levels "negative","neutral",..: 2 3 2 1 1 1 3 2 3 3 ...

In this table you can see the total of the dataset how many tweets are negative, how many positive and how many neutrals. In this case we have 9178 negative tweets, 3099 neutral and 2363 positive so in cocnlusion we have to predominate the negative tweets because there is not much conformity with the services of American Airlines.

Percent by airline.

library("ggplot2")

## Warning: package 'ggplot2' was built under R version 3.3.2

ggplot(airlines, aes(x=factor(airline),fill=factor(airline_sentiment)))+
  geom_bar(position="dodge")+xlab("")+ylab("Mensajes")+
  labs(fill = "Clasificación")

Naive Bayes is an algorithm used for the classification of text which can be trained to improve its rating capacity. Your classification is based on probabilities, taking into account the probabilities that each of the attributes exists and thus obtaining a total probability of whether it belongs to one group or another. In the text classification it calculates the probability of whether there is a word or not in the text to be sorted.

Naive Bayes Applications

The applications of Naive Bayes can be many as long as our goal can be classified. Some examples are:

• News Classification
• Comments Clasification
• Medicine
• Geology
• Presidencial Elections
• Trends in Social Networks
• Musical tops
• Weather
• Approval of credits
• Spam
• Ham

A corpus is the creation of text documents. We’re going to create a corpus, using the VCorpus() command to create a text vector from the airlines.

library("tm")
airlines_corpus <- VCorpus(VectorSource(airlines$text))
print(airlines_corpus)

## <<VCorpus>>
## Metadata:  corpus specific: 0, document level (indexed): 0
## Content:  documents: 14640

Corpus check

inspect(airlines_corpus[1:2])

## <<VCorpus>>
## Metadata:  corpus specific: 0, document level (indexed): 0
## Content:  documents: 2
## 
## [[1]]
## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 35
## 
## [[2]]
## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 72

as.character(airlines_corpus[[ 1]])

## [1] "@VirginAmerica What @dhepburn said."

lapply(airlines_corpus[1:3], as.character)

## $`1`
## [1] "@VirginAmerica What @dhepburn said."
## 
## $`2`
## [1] "@VirginAmerica plus you've added commercials to the experience... tacky."
## 
## $`3`
## [1] "@VirginAmerica I didn't today... Must mean I need to take another trip!"

Given that taxpayers tweets may contain numbers, punctuation, or blanks. You have to clean the tweets by removing the aforementioned. For this part we will use the Library “TM” which helps us to clean the data.

library("tm")

airlines_corpus_clean <- tm_map(airlines_corpus,content_transformer(tolower))
airlines_corpus_clean <- tm_map(airlines_corpus_clean,removeNumbers)
airlines_corpus_clean <- tm_map(airlines_corpus_clean,removeWords,stopwords())
airlines_corpus_clean <- tm_map(airlines_corpus_clean,removePunctuation)
airlines_corpus_clean <- tm_map(airlines_corpus_clean,stemDocument)
airlines_corpus_clean <- tm_map(airlines_corpus_clean, stripWhitespace)
airlines_dtm <- DocumentTermMatrix(airlines_corpus_clean)