Politeness Levels of Thanking and Apologizing: A Corpus Linguistic Investigation Across Generations in the Movies

The levels of politeness in expressing thanking and apologizing connect to the concept of language attitude within sociolinguistics. Language attitude in sociolinguistics refers to an individual’s mental disposition or feelings towards a particular languag e. It implies that a person's language attitude can range from being favorable, and indifferent, to unfavorable influenced by the environment. Based on the corpus linguistics, this study aimed to investigate politeness levels of thanking and apologizing in American movies across generations, from Baby Boom to Alpha Generation using a corpus software, AntConc, as a tool to process the necessary data. The result of this study showed that the levels of politeness thanking, and apologizing had different choices of words in the movies of each generation. In addition, this was due to factors such as environment and knowledge of the rules of expressions such as Thanks or Thank you, and Sorry or Pardon


INTRODUCTION
This study was inspired by two videos on Instagram that were uploaded on 9/29/22 and 10/8/22 by the account @taka_nihongo_dojo (Taka, 2022).The videos show some politeness levels of thank you and apologies in Japanese.Basically, politeness is the strategy for interacting with other people by minimizing their facethreatening acts (FTA) (Brown & Levinson, 1987:91).In other words, politeness is a way in linguistics to minimize the threatening face of the hearer in conversation (Syifa et al., 2021:78).Although politeness study is a part of pragmatics, it should be studied with the light offered by sociolinguistics as well (Christie, 2015:356).
However, Jaeger (2019) supported the idea that the concept of politeness is used to include expressions such as "thank you" and "apologies."Thus, it can be inferred that expressions such as thank you and apologies are politeness in pragmatics, but studying the politeness form of pragmatics cannot exclude sociolinguistics.
After careful observation of the Instagram videos, it can be seen that the actor's body gestures and facial expressions showed a difference in politeness levels.On the other hand, politeness levels of thanking and apologizing are sorted from the polite to the more polite.For instance, "thanks" is a form of expression of gratitude whose level of politeness is at a general or ordinary level."Thanks" is used for simple and straightforward thanking (Jacobsson, 2002).Meanwhile, "thank you" is an expression of gratitude with a politeness degree that is higher than "thanks." Jacobsson argued, "The expression of thank you is a positive affective speech act" (2002:65).In other words, the expression "thank you" is a speech act that refers to someone's positive feelings.For politeness levels of apologizing, "sorry" is the most common expression of regret (Leech, 2014).Therefore, this expression is on the first level.In addition, the "pardon" expression, requesting forgiveness, is realized by means of a highly conventionalized and thus accepted imperative construction (Leech, 2014).Thus, the expression "pardon" has a higher level of politeness than the expression "sorry." Those levels of politeness are related to the concept of language attitude in sociolinguistics.Language attitude in sociolinguistics discusses a person's mental condition toward a language.It means the language attitude of someone can be
Holmes further claimed that there is a key idea in language attitude called determinism, which is language attitude as a result of environmental factors.In addition, people generally find it easier to understand languages and dialects spoken by people they like or admire (Holmes, 2013).Therefore, this language attitude can be understood as the tendency of a person's positive attitude towards a language in the immediate environment, such as a certain era or generation.
From the World War II period until the present, there have been six generations, ranging from the 1920s to the present.1) The veteran generation was born in the 1925s-1946s: 2) The baby boom generation was born in the 1946s and 1960s; 3) The X generation was born in the 1960s and 1980s; 4) The Y generation was born in the 1980s and 1995s.5) The Z generation was born in 1995-2010, and the Alpha generation was born in the 2010s (Yanuar, 2016:130).
To find out examples of thanks and apologies utterances, one of the easiest ways is by observing them in the object of study, such as in a movie.Dialogue or conversation in a movie is available to us with the help of an internet search engine.
However, movies from the 1920s were still rare and hard to find.Therefore, the conversations in the movies from the 1946s generation to the 2022s generation will be the object of this study.Furthermore, there are several relevant previous studies for this current study.In the first previous study, apologies in speakers of South Indian and Sri Lankan English were analyzed using a corpus engine (Degenhardt & Bernaisch, 2022).To get the data, they used the International Corpus of English (ICE).The results of this study indicate quantitative differences in the use of sorry, which are influenced by factors such as type of apology, topic, age, or combinations of said factors.In addition, a second previous study by Jaeger (2019) used The Movie Corpus, which is already available on the internet and can be accessed for a fee, of course, and it was used to find politeness expressions in children's movies.
The result of Jaeger's study was that politeness markers were and had tended to be more common in children's movies than in other genres of movies.Lastly, Saengkaew (2016) conducted a study that was used as a research source to support the current study.The corpus engine (COCA) was employed in this study to identify the thankful utterances technique.This study showed that thank-you was the most frequently used strategy, which may explain why most authors include it in conversation textbooks, while non-gratitude is the least frequent strategy.
Based on this perspective, this study will examine the politeness levels of thanking and apologizing in the movies from the Baby Boom to the Alpha Generations by using the corpus software as a tool to process the necessary data.
The purpose of this study is to compare how different generations communicate their thanks and apologies in terms of politeness levels.This study is expected to be able to provide additional knowledge of different generations and the language attitudes of each generation towards different levels of politeness in thanking and apologizing with the help of the corpus engine as a tool to collect the necessary data.

RESEARCH METHODS
The corpus linguistics approach is used in this study because it allows for the calculation of the frequency of politeness levels of thanking and apologizing in vast corpora of data (Litosseliti, 2010).Furthermore, the main data of this study is the word "thanks," "thank you," "sorry," and "pardon" in American movies from 1946 to 2022.There are several American movies from each generation based on the theories of generation.Additionally, there are some supporting data, such as previous studies, references to theories, and relevant papers, to answer the research purpose.For the data sources, the following is a list of movie titles from the Baby Boom generation to the Alpha generation: 1.This study employs corpus linguistics as its primary method for data collection.
The corpus linguistic approach has been widely acknowledged as a powerful and flexible tool for investigating language acquisition, processing, variation, and evolution, as highlighted, and it can create its own corpora.(Rose et al., 2020;Podesva & Sharma, 2013;McEnery & Hardie, 2012;Litosseliti, 2010).This study

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comprises two main components: the first segment focuses on corpus construction, or the steps before processing the data, while the subsequent part delves into the utilization of corpus linguistics methodologies. 5. Manage the data by using AntConc.

Concordance Hit as a Corpus Analytic Tool to Find the Frequency
In this study, there are four words chosen to be seen in each generation.Expressions of gratitude as level 1 is Thanks, and level 2 is Thank you.For apologizing expressions like level 1 is Sorry, and level 2 is Pardon.

AntConc Process
At this stage, the TXT data that has been collected, and differentiated by folder for each generation is opened into the Antconc application.This process is carried out in stages starting with the Baby Boom generation folder and ending with the Alpha generation folder.This process is done by finding the frequency of the number of words that have been selected from each generation.

b. Technique of Data Analysis
According to Miles et al. (2014), there are three steps in analyzing the data in a corpus study: 1) data condensation; 2) data display; and 3) conclusion drawing and verification.
1. Data condensation refers to the process of selecting, focusing, simplifying, abstracting, and/or transforming the data that appear in the full corpus of written-up field notes.2. The data displayed an organized compressed assembly of information, allowing conclusion drawing and action.

Conclusion Drawing and Verification
. the data of this study are analyzed, and the result can be as the new hypothesis or the knowledge of corpus linguistics approaches to politeness level of thanking and apologizing in linguistics attitude.

FINDINGS a. Number of Thanking and Apologizing in the Movies
The following table and chart show the number of selected words by their generation.Table 1 presents the number of thanking and apologizing expressions in American movies and their generations.Tokens mean the number of words, and concordance hits is the tool in AntConc software that presents the frequency of tokens.In chart 1, it presents the highest and lowest frequency of tokens among its generations.Pardon 5

Chart 1. Concordance Hits of expression in the movies and its generations
From the table and chart above, it can be seen that the highest frequency of expressions of "thanks" is in the Baby Boom generation movies, with a total of 107 tokens, while the lowest is in the Alpha generation movies, with a total frequency of 56 tokens.Meanwhile, from the graph above, the frequency of Thank You does not have much difference that appears in X generation, Y, and Z movies.In addition, the frequency of expressions of thank you is most prevalent in X-generation movies, with a total of 227 tokens, and not much different in Baby Boom, Millennial, and Alpha-generation movies.The frequency of Thank You in the Z generation is the least compared to other generations, namely 164 tokens.The highest frequency of sorry expressions is in Alpha-generation movies, with a total of 283 tokens, while the lowest frequency is in X-generation movies, which only have 132 tokens.In the Baby Boom generation, this is the second-lowest amount of Sorry, with 142 tokens.
Nevertheless, the second highest number of sorry expressions is in Z-generation movies, with 243 tokens.Furthermore, the next largest frequency is Y-generation movies, with 196 tokens.Finally, the frequency of pardon expressions in the Baby Boom generation is the highest, which amounts to 29 tokens.Meanwhile, the graph above shows that X-generation movies have the second-largest number of tokens, and Z-generation movies are the third.Lastly, movies are in the Y generation and Alpha, which have almost the same number of tokens.

DISCUSSION
Our study's findings on the frequency of Thanksgiving and apologies in films across generations show similarities to earlier research while also revealing unique patterns affected by generational traits.Our findings about apologies are consistent with those of Degenhardt and Bernaisch's research (2022), which highlighted cultural differences in apologetic tactics.We found that films starring members of the Alpha generation had the highest prevalence of apology scenes, which may be a result of the generation's increased use of digital communication and less faceto-face connection.The X-generation, in contrast, who are renowned for their adaptability and independence (Jurkiewicz, 2000), showed a reduced frequency of apologies, possibly as a result of their self-reliance and attention to their own image.
Our results contrast with Jaeger's study (2019)

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societal standards of politeness have changed through time.This may be related to the Baby Boomers' upbringing, which was characterized by an emphasis on respect and good manners (Howe & Strauss, 1991).The Alpha generation, on the other hand, had the lowest frequency of gratitude, maybe as a result of their diminished emphasis on social relationships and thanks.The Alpha generation is known for immediate gratification and individuality (McCrindle, 2015).
Numerous limitations and difficulties we ran across during the data collection procedure may have had an impact on the outcomes.First off, there's a chance that the distribution of movies from each generation wasn't equal, which raises the issue of sample bias.The frequency of sentiments of thankfulness and regret might also vary depending on the genres, subjects, and target audiences of the movies.We acknowledge that the genre-specific variations are not taken into account in our research.
In addition, character conduct towards showing thanks and apologizing may have been influenced by the setting of the movies, especially the cultural and societal standards portrayed in them.This contextual variance could conceivably explain some of the observed differences, even though we did not specifically account for it in our research.
Even though the generational traits we examined fit with our interpretation of the data, it is crucial to take into account other possibilities.The observed variances in expressions of thanks and apologies may be attributed to a variety of factors, including the setting of the films, regional differences, and specific character features of the movie characters.However, given that those generational qualities are expected to consistently affect behavior in a range of circumstances, our attention to these traits is still important.
It is important to recognize the constraints of our research and the particular context in which our findings should be interpreted.Based on character dialogue from movies, our research reflects staged exchanges.Real-life language and conduct may differ greatly from what is depicted in films.Furthermore, our investigation focuses solely on thank-you and apology expressions, which are just one aspect of civility and interpersonal communication.We have included pertinent literature to support our interpretations and arguments throughout this debate, including works by Degenhardt and Bernaisch (2022), Jaeger (2019), and Saengkaew (2016).In order to put our findings into context, these studies offer insightful information about the dynamics of politeness and the displays of thankfulness and regret.
As a result, we have found fascinating patterns influenced by generational characteristics in the professions of thankfulness and apologies in generational movies.While some of our findings are consistent with other studies, they also provide new insight into specific generational dynamics.We acknowledge the need for additional study to delve further into the subtleties of courtesy and communication within various age situations.

CONCLUSION
Overall, this study examined how polite sentiments of thanks and regret were expressed by people of different generations in American movies from 1946 to 2022.This study, which employed a corpus linguistics methodology, revealed notable patterns influenced by the distinctive qualities of each generation, providing insightful information about how societal politeness rules have changed through time.The results supported the notion that generational differences and cultural changes have a big impact on how people express regret in their apologies.
Furthermore, the Alpha generation, known for their significant reliance on digital communication and fewer in-person meetings, displayed the most frequent apologies.However, while being known for their independence and self-reliance, the X generation apologized less frequently, which may be related to their great emphasis on preserving a favorable personal image.

1.
Collect the data by downloading conversation scripts for each movie from 1946-2022.2. Divide it in each generation by folder.3. Change the format from SRT to TXT. 4. Carry out the cleaning process.