Look Who’s Talking: The Most Frequently Used Words in the B ulgarian Parliament 1990-2024 (2024)

Abstract: In this study we identify the most frequently used words and some multi-word expressions in the Bulgarian Parliament. We do this by using the transcripts of all plenary sessions between 1990 and 2024 - 3,936 in total. This allows us both to study an interesting period known in the Bulgarian linguistic space as the years of “transition and democracy”, and to provide scholars of Bulgarian politics with a purposefully generated list of additional stop words that they can use for future analysis. Because our list of words was generated from the data, there is no preconceived theory, and because we include all interactions during all sessions, our analysis goes beyond traditional party lines. We provide details of how we selected, retrieved, and cleaned our data, and discuss our findings.

## object and motivation the political changes in bulgaria in 1989 led to demands for greater transparency of political power, including the right of public access to information. as a result, transcripts of meetings of the national assembly ( 1 ) were made public, but only after considerable public pressure. so far, these transcripts have mainly been used for qualitative analysis of individual debates on a case-by-case basis. they have rarely been considered as a corpus in their own right, most likely due to the considerable number of transcripts available (every transcript from 27 february 1879 -just 17 days after the national assembly was established -and onwards is available) as well as the way they were 1 https://www.parliament.bg/bg/plenaryst made accessible (each had to be downloaded individually). here, we will use natural language processing (nlp) methods to study this corpus as a whole, allowing us to identify the most frequently used words in the national assembly between 1990 and 2024. we do this for both theoretical and methodological reasons. the theoretical reasons include gaining a better understanding of the topics discussed by parliamentarians. according to salience theory @xcite , frequently used words are of greater importance to speakers and can provide insight into the interests of the national assembly. this is particularly relevant as the period under study witnessed significant and structural changes in bulgarian politics and society. methodological reasons include our desire to generate a list of stop words that future researchers can use to further preprocess this corpus to better estimate any concepts of interest, as well as to provide an example of how this data can be used for other nlp-related problems. ## background transcripts of legislative debates are often used to study the opinions, positions and policy preferences of elected politicians (abercrombie and batista-navarro, 2020). in the bulgarian context, the focus is often on individual political speeches and the debate in which they were made. thus, studies have been conducted on the use of foreign words @xcite , the media behaviour of the political elite @xcite , linguistic aggression @xcite @xcite , the appearance of european identity @xcite , the use of clichés, dialects and factual errors @xcite , the quantitative ratio of words from one national assembly to another @xcite , and the language of certain mps @xcite . in addition, various attempts have been made to expand the current corpus. for example, @xcite provide an annotated version of part of the corpus; @xcite use the audio of the speeches to build a new corpus of bulgarian speech suitable for training and evaluating modern speech recognition systems; and the strazha foundation will combine it with the duration of each session, the number of words by party, the average number of words per mp by party, the most verbose mps and other related facts to discuss and comment on the current state of the national assembly ( foot_0 ). finally, transcripts from 2015 onwards have been made part of the parlamint dataset @xcite , in which each political speech is annotated with, among other things, the age, gender and political orientation of the speakers. as parlamint contains similar data from 17 european national parliaments, this allows for cross-country comparisons, as shown by @xcite . one thing these transcripts have not been used for is to examine the frequency of word choice. this is interesting, as this is often seen as one of the basic requirements for understanding the corpus (o' @xcite . as a result, a domain-specific list of words that can be used as stop words is missing, as this requires recourse to the corpus one wishes to use @xcite . thus, the creation of such a list can help scholars to better deal with the data from these transcripts and make future analyses less complicated. ## data and pre-processing each of the 3,936 minutes is structured in the same way. first, the chair and vice-chair and the secretary are identified, together with the date and time of the meeting. then each speaker is identified individually and their remarks are listed. this includes both what they say and what else is happening in the meeting at the same time. however, while noise or applause is included with general remarks, the specific insults and attacks from the floor are not @xcite . the transcripts do not record the insults exchanged by the deputies in the chamber, but only those uttered from the gallery. the meetings themselves have no particular structure -sometimes votes are followed by further discussion; sometimes meetings begin with an agenda, but not always; sometimes they begin with proposals to change the agenda; and sometimes there are agenda items listed at the beginning. after downloading the individual transcripts from the national assembly website ( foot_1 ), we convert them from html to txt format. we fix any encoding problems and remove headers and footers. next, we tokenise our words (this and all subsequent steps are performed using version 3.3.1 of the quanteda package in r @xcite ), lowercase them, generate n-grams to capture common expressions, remove punctuation, symbols and numbers, and finally remove stop words as contained in the bultreebank corpus @xcite . this last step is crucial, as failure to do so would result in the identification of stop words that are common to bulgarian in general, rather than those that are specific to the national assembly. it also prevents our multi-word expressions (mwes) from consisting solely of collections of frequently used words and expressions. this results in a corpus of 694,174 unique tokens. for our purposes here, we focus on the 250 most frequent words in this resulting data set (the last of which had a relative frequency of 0.033%), although this cut-off is necessarily arbitrary. appendix a provides an overview of these words, together with an english translation. ## results as a result, we get a list of words and some typical mwes for parliamentary speeches. there is no specific study of mwes in this analysis. however, mwes and their derivatives play an important role in certain topics when nlp methods are used @xcite . the list is rich with collocations typical for parliamentary life such as "уважаеми дами господа народни представители" (respectfully, ladies and gentlemen deputies), "предложението прието" (the proposal is accepted). through a political-historical prism we can distinguish nine groups of meaning-functional types of words in the resulting list: a) legal terms; b) places and countries; c) financial; d) parliamentary behaviour; e) procedural; f) verbs; g) adverbs; h) party abbreviations; j) other. these types are not surprising. in an earlier study on the bulgarian language in general, @xcite found that the most commonly used nouns are those related to time, place and people. the most common type (in terms of frequency) are words related to law, where the two abbreviations "aл" (paragraph) and "чл" (article) are the most common, followed by "закон" (law), "запонопроект" (draft) and "предложение" (proposal). this is followed by geographical references. unsurprisingly, the word "българия" (bulgaria) is the most frequently used, followed by related terms such as "страна" (country), "държава" (state), "република" (republic), "българските" (bulgarian -adjectival) and "граждани" (citizen). bulgarian as a nationality does not appear in this list of most frequently used words, but can be found instead in references to "общество" (society) or "хора" (people). more geographical references -such as "европейският съюз" (european union) and "софия" (sofia) -can also be found. it is noteworthy that @xcite found similar terms, suggesting that these terms have changed little in importance over time. another common category is financial references -most often to the bulgarian currency ("лв"). we also find words such as "пари" (money), "бюджет" (budget), "хиляди" (thousands) and "милиони" (million). note that there are no references to other currencies. this suggests that the debate on the adoption of the euro as the official currency is not (yet) dominant during the period we are studying. next, we find words that demonstrate politeness and respect for colleagues @xcite see also), where we find words such as "уважаеми" (dear), "моля" (please), and "благодаря" (thank you). this kind of politeness is often nothing more than a set of linguistic conventions that operate independently of the current goal a speaker is trying to achieve @xcite . as such, this type of politeness is more operational, helping politicians to introduce themselves, rather than reflecting their opinions of each other. related to this are words that refer to different parliamentary procedures, such as "решение" (decision), "гласуване" (voting), "комисия" (commission), "изказвания" (speeches), "предложения" (suggestions), "въпрос" (question), "процедура" (procedure), реплики (replies), and "текстове" (texts). two other categories are verbs and adverbs. under the former, we find words like "мисля" (think), "казвам" (say), "смятам" (consider), "разбира" (understand), and "искам" (want), and under the latter words such as "всъщност" (in fact), "наистина" (really), "ясно" (clearly), "просто" (simply), "тоест" (i.e.), "действително" (actually), "изключително" (exceptionally), and "вярно" (truly). interestingly, there are no verbs expressing insistence. instead, the imperative particle "нека" (let us) is often used. moreover, the tendency to use impersonal constructions also shows that parliamentarians seem to be trying to avoid personal responsibility, opting instead for general responsibility. finally, we find references to the parties. interestingly, although the corpus consists of texts from more than 30 years, the word герб -an abbreviation of one of the political parties -is also among the most frequently used words ("граждани за европейско развитие на българия" -citizens for european development of bulgaria). and while the word "герб" can also refer to a coat of arms, in the parliamentary context here there is no doubt that the disambiguation of the word refers to the political party. ## conclusions and future work the analysis of a corpus of bulgarian parliamentary speeches reveals some interesting findings: bulgarian politicians use bulgaria prominently in their speeches; terms such as "european" are also important, but not as central as "bulgarian"; the speeches also show linguistic politeness, presumably as a convention. ab-proceedings of clib 2024 breviations related to law are common, as are terms describing procedures in legislative tasks. verbs indicating cognitive effort are widespread, but the frequent use of the imperative particle "нека" (let us) suggests a tendency to defer decision-making or responsibility. the abbreviation for the bulgarian currency is noteworthy, while the dominance of the abbreviation for the political party "герб" reflects the dominance of this particular party, despite the presence of others in parliament during the period analysed. the generated list contains meaningful words such as "budget", "decision", "abstention", "understand", which are semantically relevant and essential and cannot be considered as stop words. however, the additional list provided can be used for specific purposes for further automated linguistic analysis with a different focus: for example, for more in-depth analysis of the main themes in the contemporary development of politics and public attitudes in bulgaria after the beginning of the democratic changes. the large dataset allows for the study of how language has changed over the years, as well as for comparative analysis of the language of individual parties on particular issues. a more in-depth study can reveal the mwes in parliamentary speech and their pragmatic role.