This document analyzes consumer complaints data about Comcast from two sources. It first explores Comcast consumer complaints data from ConsumerAffairs, examining word frequencies and variations in complaints across states. It then analyzes FCC complaints data about Comcast from 2015, finding that states like Florida and California had the most complaints, and cities like Atlanta and Chicago led in number of complaints. Word clouds are generated to visualize common words in low and high-rated complaints for both data sets.

1. Comcast Consumer Complaints
A first approach in R language
Olabanji Shonibare
oyshonib@mtu.edu

2. Natural Language Processing (NLP)
NLP is a set of techniques for approaching text
problems

3. Natural Language Processing (NLP)
A few questions
• Word frequency
• Variation across states

4. Comcast Consumer Complaints
• comcast_consumeraffairs_complaints.csv
• comcast_fcc_complaints_2015.csv
Raw complaint data about Comcast television and internet
published at consumeraffairs.com between 04/08 and 09/16.
Raw complaints made to the FCC about Comcast between 04/15
and 06/15.

5. Preliminaries
df <- read.csv("comcast_consumeraffairs_complaints.csv")
df_fcc <- read.csv("comcast_fcc_complaints_2015.csv")
dim(df)
## [1] 5659 4
names(df)
## [1] "author" "posted_on" "rating" "text"
dim(df_fcc)
## [1] 2225 11
names(df_fcc)
## [1] "Ticket.." "Customer.Complaint"
## [3] "Date" "Time"
## [5] "Received.Via" "City"
## [7] "State" "Zip.code"
## [9] "Status" "Filing.on.Behalf.of.Someone"

6. Comcast Consumer Affairs Complaints

7. ggplot(df) +
geom_bar( aes(x=rating))

8. df %>% group_by(rating) %>% summarise(count = n())
## # A tibble: 6 × 2
## rating count
## <int> <int>
## 1 0 1560
## 2 1 3734
## 3 2 260
## 4 3 54
## 5 4 19
## 6 5 32
df2 <- df %>% filter(rating != 0)
ggplot(df2) +
geom_bar( aes(x=rating))

9. df3 <-
df2 %>%
mutate( State = str_sub(toupper(author), -2))
df3 %>%
group_by(State) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
## # A tibble: 52 × 2
## State Count
## <chr> <int>
## 1 FL 650
## 2 CA 345
## 3 GA 320
## 4 IL 284
## 5 PA 221
## 6 TN 202
## 7 TX 193
## 8 MI 189
## 9 WA 168
## 10 NJ 167
## # ... with 42 more rows

10. low_rating <-
df2 %>%
filter(rating < 3)
high_rating <-
df2 %>%
filter(rating >= 3)
nrow(low_rating)
## [1] 3994
nrow(high_rating)
## [1] 105
#cs_ratio: customer satisfaction ratio
df3 %>%
select(State, rating) %>%
group_by(State) %>%
summarise(cs_ratio = length(rating[rating>2])/length(rating)) %>%
arrange(desc(cs_ratio))
## # A tibble: 52 × 2
## State cs_ratio
## <chr> <dbl>
## 1 IA 1.00000000
## 2 ID 1.00000000
## 3 BC 0.50000000
## 4 NV 0.33333333
## 5 WV 0.13333333
## 6 NH 0.10714286
## 7 MO 0.09090909
## 8 ER 0.06666667
## 9 SC 0.05263158
## 10 AZ 0.05000000
## # ... with 42 more rows
States with high customer satisfaction ratio (rating >2)

11. Word cloud for low ratings
low_stops <- c('comcast', stopwords("english"))
low_ratingCorpus <-
Corpus(VectorSource(low_rating$text)) %>%
tm_map(removePunctuation) %>%
tm_map(removeNumbers) %>%
tm_map(tolower) %>%
tm_map(removeWords, low_stops) %>%
tm_map(removeWords, stopwords("english")) %>%
tm_map(stripWhitespace) %>%
tm_map(PlainTextDocument)
#tm_map(stemDocument)
wordcloud(low_ratingCorpus, scale=c(5,0.5), max.words=100, random.order=FALSE,
rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, "Dark2"))

12. Word cloud for low ratings
low_stops <- c('comcast', stopwords("english"))
low_ratingCorpus <-
Corpus(VectorSource(low_rating$text)) %>%
tm_map(removePunctuation) %>%
tm_map(removeNumbers) %>%
tm_map(tolower) %>%
tm_map(removeWords, low_stops) %>%
tm_map(removeWords, stopwords("english")) %>%
tm_map(stripWhitespace) %>%
tm_map(PlainTextDocument)
#tm_map(stemDocument)
wordcloud(low_ratingCorpus, scale=c(5,0.5), max.words=100, random.order=FALSE,
rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, "Dark2"))

13. Word cloud for high ratings
temp_stops <- c('comcast', stopwords("english"))
high_ratingCorpus <-
Corpus(VectorSource(high_rating$text)) %>%
tm_map(removePunctuation) %>%
tm_map(removeNumbers) %>%
tm_map(tolower) %>%
#tm_map(removeWords, low_rating_stops) %>%
tm_map(removeWords, temp_stops) %>%
tm_map(stripWhitespace) %>%
tm_map(PlainTextDocument)
#tm_map(stemDocument)
wordcloud(high_ratingCorpus, scale=c(5,0.5), max.words=100,
random.order=FALSE, rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, "Dark2"))

14. Comcast Fcc Complaints (2015)

15. # State vs number of complaints
temp1 <-
df_fcc %>%
group_by(State) %>%
summarise(no_complaints = n()) %>%
arrange(desc(no_complaints))
#temp1
ggplot(temp1, aes(x=State, y=no_complaints)) +
geom_bar( stat ="identity") +
scale_x_discrete(limits = temp1[["State"]]) +
theme(axis.text.x = element_text(angle = 60, hjust = 1))

16. # grab the first 10 rows
temp2 <-
temp1 %>%
slice(1:10)
ggplot(temp2, aes(x=State, y=no_complaints)) +
geom_bar( stat ="identity") +
scale_x_discrete(limits = temp2[["State"]]) +
theme(axis.text.x = element_text(angle = 60, hjust = 1))

17. temp3 <-
df_fcc %>%
group_by(City) %>%
summarise(no_complaints = n()) %>%
arrange(desc(no_complaints))
## # A tibble: 928 × 2
## City no_complaints
## <fctr> <int>
## 1 Atlanta 63
## 2 Chicago 47
## 3 Knoxville 36
## 4 Houston 33
## 5 Jacksonville 31
## 6 Philadelphia 25
## 7 Denver 22
## 8 Miami 22
## 9 Nashville 22
## 10 Indianapolis 21
## # ... with 918 more rows

18. ggplot(temp3, aes(x=City, y=no_complaints)) +
geom_bar( stat ="identity") +
scale_x_discrete(limits = temp3[["City"]]) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 1))

19. temp4 <-
temp3 %>%
slice(1:20)
## # A tibble: 20 × 2
## City no_complaints
## <fctr> <int>
## 1 Atlanta 63
## 2 Chicago 47
## 3 Knoxville 36
## 4 Houston 33
## 5 Jacksonville 31
## 6 Philadelphia 25
## 7 Denver 22
## 8 Miami 22
## 9 Nashville 22
## 10 Indianapolis 21
## 11 San Francisco 20
## 12 San Jose 20
## 13 Baltimore 19
## 14 Tucson 19
## 15 Washington 19
## 16 Marietta 16
## 17 Portland 16
## 18 Seattle 14
## 19 Memphis 13
## 20 Canton 12

20. ggplot(temp4, aes(x=City, y=no_complaints)) +
geom_bar( stat ="identity") +
scale_x_discrete(limits = temp4[["City"]]) +
theme(axis.text.x = element_text(angle = 60, hjust = 1))

21. df_fcc %>%
group_by(Customer.Complaint) %>%
summarise(no_complaints = n()) %>%
arrange(desc(no_complaints))
## # A tibble: 1,842 × 2
## Customer.Complaint no_complaints
## <fctr> <int>
## 1 Comcast 83
## 2 Comcast Internet 18
## 3 Comcast Data Cap 17
## 4 comcast 13
## 5 Comcast Billing 11
## 6 Comcast Data Caps 11
## 7 Data Caps 11
## 8 Unfair Billing Practices 9
## 9 Comcast data cap 8
## 10 Comcast data caps 8
## # ... with 1,832 more rows

22. all_stops <- c(‘comcast','now','company','day','someone','thing','also',
'got','way','call','called','one','said','tell', stopwords("english"))
df_fccCorpus <-
Corpus(VectorSource(df_fcc$Customer.Complaint)) %>%
tm_map(removePunctuation) %>%
tm_map(removeNumbers) %>%
tm_map(tolower) %>%
tm_map(removeWords, all_stops) %>%
tm_map(stripWhitespace) %>%
tm_map(PlainTextDocument)
#tm_map(stemDocument)
wordcloud(df_fccCorpus, scale=c(5,0.5), max.words=100, random.order=FALSE,
rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, "Dark2"))

23. https://www.kaggle.com/dan195/d/archaeocharlie/
comcastcomplaints/first-run
Credits: