write codes on Rstudio using the table and the below;

Table 3 presents the correlation results for key customer service attributes.

Table 3: Correlation Matrix of Customer Feedback Keywords

Quality

Service

Price

Selection

Cleanliness

Atmosphere

Convenience

Location

Staff

Wait Time

Quality	1.00	0.45	-0.23	0.57	0.38	0.32	0.24	0.15	0.18	-0.05
Service	0.45	1.00	0.12	0.65	0.51	0.46	0.39	0.28	0.62	0.21
Price	-0.23	0.12	1.00	-0.35	-0.26	-0.15	-0.20	-0.31	-0.08	0.13
Selection	0.57	0.65	-0.35	1.00	0.43	0.38	0.31	0.12	0.23	-0.10
Cleanliness	0.38	0.51	-0.26	0.43	1.00	0.49	0.36	0.21	0.29	-0.05
Atmosphere	0.32	0.46	-0.15	0.38	0.49	1.00	0.27	0.18	0.25	-0.08
Convenience	0.24	0.39	-0.20	0.31	0.36	0.27	1.00	0.34	0.21	-0.15
Location	0.15	0.28	-0.09	0.22	0.15	0.18	0.30	1.00	0.18	-0.13
Staff	0.18	0.62	-0.08	0.23	0.29	0.25	0.21	0.18	1.00	0.12
Wait Time	-0.05	0.21	0.13	-0.10	-0.05	-0.08	-0.15	-0.13	0.12	1.00

Source: Customer Survey Analysis (2023)

Note: The values in the table represent the Pearson correlation coefficient between the corresponding pairs of feedback keywords.

# Perform sentiment analysissentiment <- inner_join(as.data.frame(dtm), customer_data, by = c("row.names" = "feedback"))sentiment <- sentiment[, c("row.names", "SentimentScore")]

# Perform topic modelinglda <- LDA(dtm, k = 5) # Adjust the number of topics (k) as needed

# Extract topics from the modeltopics <- tidy(lda, matrix = "beta")topics <- topics %>% group_by(topic) %>% top_n(10, beta) %>% ungroup()

# Perform entity extractionentities <- corpus %>% unnest_tokens("entity", input = text) %>% anti_join(stop_words)

# Save the resultswrite.csv(sentiment, "sentiment_analysis_results.csv", row.names = FALSE)write.csv(topics, "topic_modeling_results.csv", row.names = FALSE)write.csv(entities, "entity_extraction_results.csv", row.names = FALSE)