Introduction

One piece is a popular manga, anime, and now live action created by Eiichiro Oda. It is one of the longest-running and most successful anime franchises in history. It follows the adventures of Monkey D. Luffy and his crew, the Straw Hat Pirates, as they search for the legendary treasure called the One Piece, which would make Luffy the King of Pirates. Along the way, they navigate complex conflicts, form deep bonds, and challenge oppressive forces.

Significance of One Piece

The popularity and longevity of One Piece make it a remarkable case study for understanding how a narrative can captivate audiences for over two decades. One of the key reasons for its enduring success is the exploration of universal themes such as Friendship, Loyalty, Rebellion, Betrayal, Sacrifice, and Dreams. These themes transcend cultural boundaries, allowing viewers to connect emotionally with the characters and storylines. By examining these themes across pivotal sagas, we can gain insights into how One Piece maintains its emotional resonance and why it continues to engage audiences across generations.

Research Question

How does the emotional tone and thematic depth of One Piece contribute to its enduring popularity and audience engagement across its sagas?

Data Collection

The metadata for this project was collected from Kaggle, which provided data scraped from IMDb. This dataset includes key information such as the season, episode number, episode title, year released, total votes, and average rating for each episode. Since One Piece has over 1,000 episodes, I focused on the most popular episodes based on their average ratings to conduct a meaningful text analysis.

However, obtaining episode transcripts presented several challenges. Popular transcript websites like Subslikescript did not have all the episodes I wanted to analyze, while Forever Dreaming provided paraphrased content instead of accurate transcripts. Additionally, since I needed English-dubbed versions, finding usable transcripts became even more complex.

To overcome these obstacles, I screen-recorded the selected episodes, converted the recordings into MP3 audio files, and in Python I utilized Whisper, an open-source speech recognition model developed by OpenAI, to transcribe the audio into text files. Whisper efficiently transcribes spoken language into written text, enabling me to generate accurate transcripts for the selected episodes and proceed with the text analysis.

Exploratory Statistics

One Piece is known as the anime that gets better over time, both in animation and story. To explore the trends in its popularity, I analyzed the average episode ratings across different years.

Lineplot 1.One Piece Average Rating Over The Years

This line chart displays the average ratings of One Piece episodes from 1999 to 2021. Each point represents the mean rating for episodes released in a given year. The chart reveals several key patterns:

1.Consistency with Gradual Growth: In the early years, the ratings remained relatively stable, hovering around 7.5 to 8.0. This suggests a consistent level of viewer satisfaction during the show’s initial growth phase.

2.Noticeable Peaks and Troughs: There are clear peaks, such as in 2010 and 2015, indicating years where episodes were particularly well-received. Conversely, occasional dips suggest periods of slower pacing or less impactful episodes.

3.Significant Rise in Recent Years: The dramatic increase in ratings around 2021 suggests renewed interest or major improvements, possibly due to enhanced animation quality or compelling story developments.

Overall, this chart highlights One Piece’s enduring appeal and its ability to captivate audiences over decades, with ratings steadily climbing as the series progresses.

Dotplot 2.One Piece Average Rating Over The Years

This dot plot displays the average episode ratings for each saga. Filler episodes not surprisingly has the lowest average rating as they are not essential to the narrative. East Blue and Alabsasta are earlier sagas and it shows exponential growth with each Saga with Wano one of the newer Saga with the highest rating. The only outlier I see is Fish-Man Island as it is the first saga after the time skip and it has the lowest low rating among the actual sagas.

Line Chart 3. One Piece Average Rating Over The Years

This line chart tracks the average episode ratings over time, segmented by saga,which is a combination of the two graphs above. It shows the earlier sagas start with moderate ratings but show improvement as the series gains momentum. Ratings fluctuate for filler episodes, reflecting their varying quality. More recent sagas show a noticeable upward trend, suggesting that One Piece has maintained or even improved its storytelling quality over the years.This visualization highlights both the series’ growth in popularity and how different sagas have impacted its ratings trajectory.

Top Saga and Episode

The top sagas were identified as —Dressrosa, Summit War, Whole Cake Island, and Wano—, known for their emotional depth, high-stakes battles, and significant plot developments. To further explore what makes these sagas stand out, I will identify the top three highest-rated episodes from each saga. By analyzing these episodes, I aim to uncover key narrative elements, pivotal moments, or character-driven events that contribute to their exceptional ratings.

## # A tibble: 12 × 6
## # Groups:   saga [4]
##    episode name                           start total_votes average_rating saga 
##      <dbl> <chr>                          <dbl>       <dbl>          <dbl> <chr>
##  1     726 Gear Fourth! Kyoui no Bounce …  2016         328            9.2 Dres…
##  2     719 Kuuchuu Kessen: Zoro Shin His…  2015         262            9.1 Dres…
##  3     663 Luffy Kyougaku: Ace no Ishi o…  2014         201            8.9 Dres…
##  4     483 Kotae o Sagashite: Hiken Ace …  2011         524            9.3 Summ…
##  5     485 Kejime o Tsukeru: Shirohige v…  2011         370            9.3 Summ…
##  6     405 Kesareta Nakama-tachi: Mugiwa…  2009         358            9.2 Summ…
##  7     958 &quot;The Legendary Battle! G…  2021         746            9.4 Wano 
##  8     892 Wano Country! To the Land of …  2019         340            9.2 Wano 
##  9     914 Finally Clashing! The Ferocio…  2019         397            9.2 Wano 
## 10     808 Kanashiki Kettou: Luffy tai S…  2017         571            9.6 Whol…
## 11     870 A Fist of Divine Speed! Anoth…  2019         683            9.5 Whol…
## 12     804 East Blue e: Sanji Ketsui no …  2017         215            9.2 Whol…

After obtain the transcripts of the 12 episodes they were transformed into a tidy format. This efficiently compiles the text into a tibble, tokenizes the text into individual words, and removes stop words and punctuation. By breaking the text down in this way, I can effectively analyze sentiments using the NRC and Bing lexicons, which will be instrumental in identifying recurring themes and emotional tones within these sagas.

Term Frequency-Inverse Document Frequency

TF-IDF is a powerful tool that highlights the significance of words within each saga in relation to others. Words with higher TF-IDF scores are particularly distinctive and strongly associated with a specific saga.

Barplot 4. Top TF-IDF Words By Saga

Dressrosa

The words “Doflamingo,” “toys,” and “underground” highlight the saga’s focus on Doflamingo’s ruthless control over Dressrosa and his ties to the black market. Terms like “fruit” and “barrier” connect to key plot points, such as Sugar’s Toy-Toy Fruit, which allowed her to turn citizens into toys, enslaving them as part of the larger scheme to produce SMILE fruits.

Summit War

“Crew,” “friends,” and “erased” reflect powerful themes of camaraderie and loss, marking the saga as the last before the time skip and the crew’s two-year separation. “Kuma,” “Sora,” and “bastard” underscore pivotal emotional moments, such as Kuma’s secret role as a double agent for both the Warlords and the Revolutionary Army.

Whole Cake Island

The words “Sanji,” “cook,” and “father” center on Sanji’s personal struggles with his family and his significant role in the story. Terms like “food” and “speak” emphasize the saga’s unique focus on diplomacy, alliances, and the importance of cuisine.

Wano

“Sword,” “blade,” and “blood” reflect the saga’s samurai-centric themes and intense battles. Meanwhile, words like “funny” and “folks” mask the tragic truth of the SMILE fruits, whose devastating effects are revealed in this arc.

The TF-IDF analysis highlights how each saga’s vocabulary reflects its core themes and narrative focus. Summit War and Whole Cake Island stand out with highly specific terms, emphasizing their distinct and emotionally charged storylines. In contrast, Dressrosa and Wano prioritize words tied to combat, leadership, and strategy, aligning with their action-packed and transformative arcs.

Bing & NRC Sentiment

Using sentiment analysis with the Bing and NRC lexicons, I will examine the emotional landscape across major sagas in One Piece. By aggregating Bing sentiment scores, I will showcase the distribution of positive and negative sentiments for each saga, providing a comparative overview of their emotional tones. Additionally, NRC sentiment analysis will highlight key sentiment categories—such as joy, fear, and anger—offering a deeper understanding of how different emotional elements shape the narrative and impact within each saga.

Barplot 5.Positive & Negative Sentiments

The chart shows that negative sentiment consistently outweighs positive sentiment across all sagas, which aligns with the intense and often dramatic events that unfold.Dressrosa has the highest negative sentiment, possibly due to the dark themes of oppression, slavery, and rebellion. A close second in negative sentiment,is Sumit Wars reflecting the climactic and tragic events of this arc, including the loss of key characters. Despite its focus on family conflict and betrayal, WCI has a relatively high positive sentiment, possibly reflecting lighter moments involving Sanji’s character arc and the unique culinary themes. While still predominantly negative, Wano shows a more balanced sentiment, likely due to moments of humor and cultural exploration alongside the grim revelations and battles.

Barplot 6.Key Sentiments

By breaking down emotional tones into these distinct key sentiments, the chart highlights the emotional complexity and narrative focus of each saga. Dressrosa stands out with high levels of anger, trust, and anticipation, which is helping draw together the themes of rebellion, hope, and camaraderie against oppression. Summit War is marked by significant fear, sadness, and anticipation. Wano features a balanced distribution, with peaks in anticipation, fear, and trust, aligning with the tension-filled buildup and alliances against Kaido. Whole Cake Island emphasizes trust and anticipation, highlighting the saga’s focus on alliances and resolutions, while joy reflects its culinary whimsy alongside underlying family conflicts.

One Piece Themes

Using sentiment analysis as a foundation, themes were derived by mapping specific emotions to broader narrative elements.

Bubble Chart 7.Saga Themes

This bubble chart visualizes demonstrates the distribution of these themes across the different sagas.

Bonds & Loyalty

Stands out as one of the most prominent themes across the sagas. This reflects the core relationships between the Straw Hat crew and their allies, emphasizing trust and camaraderie as central to the story. Time and again, the crew demonstrates unwavering loyalty, whether by risking their lives for one another or standing together against overwhelming odds.

Tyranny & Control

Highlights the darker aspects of One Piece, such as systemic discrimination, enslavement, and injustice. Arcs like “Dressrosa” and “Wano” showcase oppressive regimes, shedding light on the suffering of those under authoritarian rule. These sagas explore the consequences of such tyranny, as well as the resilience and determination of those who rise against it. By focusing on these struggles, the series provides a critique of power dynamics and the fight for freedom, adding a layer of depth to its narrative.

Oppression & Resistance

Further complements the narrative of One Piece by showcasing the ongoing battles against corrupt systems and oppressive forces. Whether it is the Revolutionary Army’s defiance of the World Government or the Straw Hats liberating oppressed communities, resistance is a central motif. This theme is especially prominent in arcs like “Summit War,” where characters’ sacrifices emphasize the high stakes and moral weight of their struggles against injustice.

Dreams & Ambition

Another recurring theme, representing the driving force behind many characters’ motivations. From Luffy’s quest to become the Pirate King to the personal aspirations of each crew member, this theme underscores the importance of determination and perseverance. This ambition is evident across sagas, particularly in arcs that delve into character backstories (WCI), reminding viewers that dreams are a source of strength even in the face of adversity.

Regression

I am using a regression to explore my research question to see how much does theme explain the variation in the average rating.In this analysis, the various themes serve as independent or predictor variables, while the average rating of each episode acts as the dependent variable. The goal is to see if narrative elements drive One Piece’s enduring appeal.

9.Regression Model

This model indicates that several themes have a significant positive impact on ratings. For instance, episodes with the theme “Bonds & Loyalty” see an average increase of 0.47 points in ratings compared to the baseline theme, while “Dreams & Ambition” and “Tyranny & Control” contribute 0.33 points and 0.37 points, respectively. On the other hand, “Freedom & Discovery” does not significantly affect ratings (p = 0.19), suggesting it may not resonate as strongly with audiences. Additionally, the total count of themes in an episode shows a small but significant negative effect, with each additional theme count reducing ratings by 0.0088 points on average (p < 0.001). The model explains 30% of the variation in episode ratings (adjusted R-squared = 0.24), emphasizing the importance of well-chosen thematic focus in driving viewer engagement, while other factors should be considered to the anime long success.

Multivariate Analysis of Variance

This is my first time using MANOVA which is a statistical test used to determine if there are any differences between groups in terms of multiple dependent variables at the same time. It is different from a one-way ANOVA which only tests one independent variable with two or more groups on a single dependent variable. Or a factorial ANOVA which tests two or more IVs on a single DV.

The MANOVA I will be conducting will have two independent variable of theme and saga to predict the dependent variable average_rating and total_count.

10.MANOVA Model

The MANOVA analysis utilizes Pillai’s trace, a statistic that ranges from 0 to 1, where higher values indicate a stronger contribution of the independent variables (theme and saga) to the model. In this analysis:

Theme has a Pillai’s trace value of 0.897, indicating a strong relationship with the dependent variables, average_rating and total_count. Saga has a Pillai’s trace value of 0.810, also suggesting a strong influence on the dependent variables. The F-tests for both independent variables evaluate whether they have statistically significant effects on the dependent variables:

The F-statistic for theme is 9.89, and for saga, it is 19.305. Both are highly statistically significant (p-values < 0.001), which means that both theme and saga have a substantial and statistically significant impact on the dependent variables at an alpha level of 0.05. These results indicate that theme and saga significantly influence average_rating and total_count, and the effects are both strong and meaningful.

However, when examining the denominator degrees of freedom (85), it is clear that there is still unexplained variance in the dependent variables that is not accounted for by either theme or saga. This suggests that while theme and saga are important contributors, additional factors may be influencing the average ratings and episode counts, warranting further investigation.

Pairwise

A pairwise comparisons must be completed after MANOVA to understand specifically how the independent variables differ in their effects on the dependent variables.

11.Pairwise Model

These results indicate where statistically significant differences exist between the themes in terms of average rating. For example, Bonds & Loyalty with Betrayal & Deception is 1. The Holm adjustment ensures that the findings are not due to chance,so no false positives (type I error) and the “1” indicates significant differences between the pairwise comparisons.

Conclusion

In conclusion, the emotional tone and thematic depth of One Piece are key factors in its enduring popularity and audience engagement. The series’ ability to balance complex emotional narratives with rich, evolving themes has contributed to its growth over the years, as evidenced by the steadily increasing average ratings across its sagas. Themes like Bonds & Loyalty, Tyranny & Control, and Dreams & Ambition resonate strongly with viewers, driving higher ratings and engagement. The statistical analysis further underscores the significant impact of both theme and saga on episode ratings, revealing how these elements work together to create a compelling, emotionally charged viewing experience that keeps audiences invested. However, while theme and saga play substantial roles, additional factors may also influence ratings, suggesting areas for further exploration in understanding the show’s long-term success.

Further Analysis

This project offers numerous opportunities for expansion and deeper exploration:

Granular Episode-Level Analysis

Conduct detailed analyses at the individual episode level, by arc, and across entire sagas to uncover more specific patterns in emotional tone and thematic development.

Character-Centric Analysis

Explore the emotional depth and thematic contributions of key characters, examining how their personal arcs influence audience engagement and ratings.

Comparative Analysis with Other Animes

Compare One Piece’s emotional tone and thematic elements with those of other long-running anime series, such as Bleach, to identify unique storytelling strategies and viewer engagement trends.

Time-Series Analysis of Anime Trends

Perform a time-series analysis to examine how trends in emotional tone, themes, and audience ratings have evolved over time, offering insights into the broader cultural and industry shifts within the anime landscape.

Citations

GeeksforGeeks. “Understanding TF-IDF (Term Frequency-Inverse Document Frequency).” Accessed December 6, 2024. https://www.geeksforgeeks.org/understanding-tf-idf-term-frequency-inverse-document-frequency/.

One Piece Wiki. “One Piece Wiki.” Fandom. Accessed December 6, 2024. https://onepiece.fandom.com/wiki/One_Piece_Wiki.

Ratingraph. “One Piece Ratings.” Accessed December 6, 2024. https://www.ratingraph.com/tv-shows/one-piece-ratings-17673/.

Silge, Julia, and David Robinson. Text Mining with R: A Tidy Approach. O’Reilly Media. Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. Accessed December 6, 2024. https://www.tidytextmining.com/.

Sonkin, Phillip. Sentiment Analysis: Welcome to Text Mining with R. Accessed December 6, 2024. https://bookdown.org/psonkin18/berkshire/sentiment.html.

Stack Exchange. “What Particular Measure to Use: Multiple Regression or MANOVA?” Cross Validated, August 2, 2012. Accessed December 6, 2024. https://stats.stackexchange.com/questions/69145/what-particular-measure-to-use-multiple-regression-or-manova.

Statistic How To. “Pillai’s Trace.” Accessed December 6, 2024. https://www.statisticshowto.com/pillais-trace/.

Statistics Solutions. “One-Way MANOVA.” Accessed December 6, 2024. https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/one-way-manova/.

ChatGPT Prompt

How to create a function to process text files for sentiment analysis? How affective are bubble charts? What are possible solutions to this error: Error in if (anova_p_value <- summary(theme_anova)[[1]][“Pr(>F)”][1] < : the condition has length > 1 I want to examine theme and average rating would a linear regression or multivariable regression would be the best?