As a passionate pizza aficionado, I often find myself engaged in spirited debates about where to find the best slice in town. Recently, I had a lively discussion with my roommates about two popular local spots Taglios and Goodfellas Pizzeria. This sparked my curiosity as while we each had our personal preferences, I wondered what the broader community thought about these establishments. To settle our debate with some empirical data, I decided to turn to Yelp. This blog post explores a comparative sentiment analysis of Yelp reviews for Taglios and Goodfellas Pizzeria, aiming to uncover not just which pizzeria is more favored by the public. To do this I will answer 3 questions surrounding this subject and come to a conclusion at the end.
Loading the CSV File and Libraries
# Loading Librarieslibrary(readr)library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidytext)
Warning: package 'tidytext' was built under R version 4.3.2
library(textdata)# Loading the CSV filereviews_df <-read_csv("yelp_reviews_output.csv")
Rows: 29 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Review, Restaurant
dbl (1): review_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Question 1
Are the sentiment trends for Taglios and Goodfellas Pizzeria showing any noticeable changes over time?
This question is intriguing because it goes beyond the average rating to examine the emotional undertones of customer reviews. By exploring the chronological sentiment trends, we can identify if either establishment has improved or declined in the eyes of its customers, or if any external factors may have influenced public perception over time.
Question 1 Data Collection Plan
I will collect review data from Yelp for both Taglios and Goodfellas Pizzeria. Each review will be associated with an ID, where ID 1 is the most recent review and ID 10 is the oldest. This reverse chronological order allows us to track sentiments over time.
Question 1 Analysis
There’s a noticeable fluctuation in sentiment scores for both restaurants over time, which suggests that customer experiences vary significantly from one review to another.
However, it is important to note that Goodfellas seems to generally have a higher sentiment score than Taglios indicating that people in the time frame of the review id’s generally seem to favor Goodfellas Pizzeria.
Question 2
What are the dominant emotions expressed in reviews for Taglio and Goodfellas Pizzeria as per the NRC Emotion Lexicon, and what does this reveal about customer experiences at these establishments?
This question is particularly interesting because it goes beyond mere satisfaction ratings and delves into the nuanced emotional reactions of customers. Understanding the dominant emotions can provide insights into the aspects that trigger specific feelings in customers. For instance, does a high frequency of ‘joy’ correlate more with food quality or service experience? By analyzing the emotional content, we can tap into the subconscious aspects of customer experiences that are rarely captured in star ratings but are often vividly expressed in the text of reviews.
Question 2 Data Collection Plan
To answer this question, I will collect and analyze text data from Yelp reviews for Taglio and Goodfellas Pizzeria. The reviews will be processed to identify and count the words associated with different emotions using the NRC Emotion Lexicon, which is a comprehensive resource for emotion analysis in text. This lexicon categorizes words into emotions such as ‘joy’, ‘trust’, ‘fear’, and others, which allows for a quantitative comparison of the emotional content in the reviews of both restaurants.
Question 2 Analysis
Joy: Joy appears to be a prominent emotion for both restaurants, with Goodfellas having a higher count. This suggests that a significant number of reviewers expressed happiness in their reviews for Goodfellas more so than Taglio.
Trust: Trust is the second most common emotion, again with Goodfellas leading slightly over Taglio. This could imply that customers feel a sense of reliability and trust towards Goodfellas.
Anticipation and Positive: These emotions are also present in a considerable number of reviews, with Goodfellas having a slight edge over Taglio. It suggests that both restaurants are generating a forward-looking, hopeful sentiment among customers.
Negative emotions (Anger, Disgust, Fear, Sadness): All these emotions are less frequently expressed, which is a positive sign for both restaurants. Goodfellas has a slightly lower count of negative emotions compared to Taglio, which could indicate a slightly better perception among its reviewers.
Surprise: This emotion has a relatively low presence in reviews for both restaurants, with Taglio having a marginally higher count. The context of the surprise (positive or negative) is not indicated by the count alone.
Negative and Positive (as a general sentiment): The category ‘positive’ has a high count for both restaurants, particularly Goodfellas, which aligns with the high counts of joy and trust. The ‘negative’ category is notably less for both, but slightly higher for Taglio, suggesting more negative sentiment expressions in its reviews.
In conclusion, the graph indicates that both Taglio and Goodfellas Pizzeria enjoy a high count of positive emotional expressions in their reviews, with Goodfellas generally having a higher count across most positive emotions. However, it’s important to consider that the counts of emotions do not necessarily reflect the intensity or context of those emotions, which could be critical in understanding the full customer experience.
Question 3
Which restaurant, Taglio or Goodfellas Pizzeria, has more positive reviews according to the sentiment analysis of Yelp reviews?
Understanding which restaurant is viewed more positively by customers is valuable for both consumers and business owners. For consumers, it can guide dining decisions, while for business owners, it offers insights into public perception and areas for improvement. This analysis extends beyond star ratings to consider the sentiment expressed in the text of reviews, offering a deeper dive into customer satisfaction.
Question 3 Data Collection Plan
I plan to collect review text data from the Yelp pages of Taglio and Goodfellas Pizzeria. By using sentiment analysis on the textual content of these reviews, I can assess the sentiment expressed by the customers more intricately than by ratings alone. The sentiment score, computed as the sum of positive and negative sentiment words, will indicate the overall sentiment of the reviews for each restaurant.
Question 3 Analysis
According to the graph, Goodfellas has a higher total sentiment score compared to Taglio. This suggests that, based on the sentiment analysis of the Yelp reviews, Goodfellas has more positive reviews than Taglio.
In summary, the total sentiment score is an indicator of the general sentiment direction of the reviews. Goodfellas’s higher score suggests that customers are expressing more positive sentiments in their reviews, implying a more favorable public perception. It is important to note, however, that sentiment analysis provides a broad picture and might not account for the context or intensity of the emotions expressed.
Conclusion
In conclusion, the analytical deep-dive into Yelp reviews for Taglios and Goodfellas Pizzeria suggests that Goodfellas may have the upper hand in the battle of public sentiment. With higher sentiment scores, a greater frequency of positive emotions such as joy and trust, and a more favorable overall sentiment, Goodfellas edges out Taglio according to the data. However, it’s worth noting that while data gives us a slice of the picture, at the end of the day a dining experience is subjective and nuanced, encompassing more than numbers can convey. Ultimately, the best pizzeria might just be the one that personally satisfies your craving for that perfect pizza.
