Slang Language Research Articles

Digital Assistant for Ventilators Using SVM Algorithm and Speech Recognition

Many health care assists had been developed to help the clinicians in treating the patient. A single monitor for managing an instrument seems very expensive. The transferring of data from the single setup also requires high communication costs. The personal assistant developed has many drawbacks due to the changes in prosodic cues according to the people’s language slang and the trouble in analysing the paralinguistic information. The network data and energy consumption required for the transfer of information from the health care devices becomes quite large. The project, involves an easy transmission module and assisting method to avoid these issues. This project is involved in assisting a practitioner, physician or respiratory therapists in proper handling of a ventilator, in accordance with patient’s health state and parameter. On providing ventilation, it is important to notice the ventilator readings such as i-PEEP, Ppeak, Pplat (developed values in the patient’s respiratory system) which are the response of the patient etc.,. On observation of these readings, the parameters such as e-PEEP, VT, RR and FiO2 (values to be set by the clinician) have to be adjusted for better ventilation and for the purpose of weaning of patient in a short period. The preliminary work involved is the data acquisition and logging. The SVM algorithm has been developed with many data points as the parameters obtained from data. The protruding idea is to analyse the patient’s age, gender, weight, disorder, type of surgery and its duration. Thus, the value of the parameter that has to be adjusted can be determined intricately with the protruding idea of digital assist.

Annotated corpus creation for sentiment analysis in code-mixed Hindi-English (Hinglish) social network data

Background: Evaluating the sentiments of tweets, blogs, comments and posts have become a crucial part of many applications. Sentiment analysis of social network data is very helpful for decision-making in application areas like movie reviews, product feedback and impact of the speech of a politician etc. The users often comment in their native languages or in slang languages or more often they use abbreviations and do not even stick to grammatical rules of the language. The bilingual and multilingual community often mixes two or more than two languages in their comments. Unavailability of annotated code-mixed data for native language adds to the difficulty in performing sentiment analysis. Objectives: The motive of this article is to present the process of creating annotated corpus for code mixed social media text in Hindi, English and Hinglish which is collected from twitter. Ambiguous meaning words and inconsistent spellings of both the languages have also been included in the study to provide wide spread canvas. Method: This study will provide significant elements that should be considered while developing the annotated corpus of Hindi, Hinglish & English dataset. The annotation is calculated on the basis of polarity of words in three categories as positive, negative and neutral. There are words which have mixed feeling i.e. these words have positive as well as negative sentiments. To consider these words, inner agreement among the polarities has been considered. The words used for sarcasm or slangs have also been taken into account. The study has included ambiguous meaning and inconsistent spelling words of both languages as well. Findings: The proposed work provides a standard annotated corpus for code switched social media text in Hindi-English (Hinglish). The process of developing the corpus and calculating the polarity has been shown. It is found that if one considers the code-mixed text, the accuracy can be enhanced. Application: The proposed corpus can be utilized in the area of market analysis, customer behavior, polling analysis, brand monitoring, etc. The corpus serves as dataset which can further be extended according to the problem definition.Keywords: Machine learning; sentiment analysis; data preprocessing; data cleaning; code-switch; linguistic-switching; multilingual

War-talk: an urban youth language of siege in Nairobi

ABSTRACT In this article, I detail how youth in poor urban settlements in Nairobi use a vernacular that I term war-talk. This is a speech, anchored in the Swahili derived urban slang language Sheng, which includes words that reference combat situations. If Sheng, as has been argued, is a generational articulation of unequal spatialized relations in Nairobi, war-talk further indexes the siege that those who live within the margins of the city experience every day, and that appears to be worsening. In addition, I put forward that war-talk is shaped by specific situated identities taken up in the East of Nairobi, subjectivities that chronicle what are seen as ongoing violations of the poor, particularly by the police. At the same time, while it bears witness to “war,” war-talk does not position its speakers solely as victims, and is performed as a language that offers deft situated escapes that portend vernacular and material agency for those who continue to be its progenitors in the margins of this city.

Slang Words in Transaction of Selling-Buying Drugs in Padang City Area

This research is able to develop linguistic aspects that are used by a group of drug dealers and users to be directly identified by people who are likely close to them. So that the public can be aware of the existence of drug sales transactions and submit it to the authorities to follow up.
 This research seeks to understand and explain the phenomena of slang used by drug dealers in buying and selling transactions Padang City. This research produces descriptive data in the form of sentences, words, or slang language phrases Padang City. This research method is Simak Libat Cakap (SLC) where researchers are directly involved in obtaining data. Sources of data in this research were obtained from oral sources. The oral data source was obtained from an interview with one dealer who was willing to be interviewed who did the distribution and had connections with other dealers.

Morphology Features of the Suffixal Slang Adverbs in the Lithuanian Language: Hybrid Suffixcal Derivatives -ai formed from the Suffixal Adjectives -iškas and from the Primary Adjectives

This article is one part of the research of adverbs in Lithuanian slang. Although there is no systematic and comprehensive research into the Lithuanian language slang, slang is commonly believed to be chaotic and have no grammatical system. The purpose of this article is to morphologically examine the derivatives formed with suffix -ai from the adjectives with suffix -iškas and from the primary adjectives without suffixes. The material (79 adverbs) is collected from the dictionary of Lithuanian slang and the dictionary of non-standard Lithuanian. The study aims to identify the systematic morphological features. To achieve the aim the research words are classified according to their suffixes, their origin is determined and morphological analysis is performed.The research reveals that a foreign language unit that emerges in the context of another language undergoes assimilation - it is transformed in one way or another by adapting it to the linguistic system. In Lithuanian slang, adverbs with the suffix -ai are mostly made up from hybrid adjectives of Russian origin with the suffix -iškas. There is also a number of adverbs formed from English hybrid adjectives. A few are derived from semantically neologic adjectives. The majority of suffixal -ai derivatives that are formed from primary adjectives are Russian origin. Adverbs which are derived from the adjectives with the suffix -iškas always retain the accent in the root: this pattern is common to all slang adverbs. All forms of adverbial degrees have accented suffixes. The findings of the reseach reveal that the morphological features of the studied slang adverbs have undergone certain transformations and follow the regularities of the Lithuanian language system: the slang adverbs copy derivative models of the standard language.

Adverbialized individual words and adverbs without suffixes in Lithuanian slang and non-normative language and their adaptative features

here are a number of adverbialized individual words and adverbs without suffixes in Lithuanian slang and non-normative language. Most of them are assimilated borrowings adapted to the language system. Adaptive adverbialization of borrowed adverbs is determined by certain systemic features which are related to the territorial dialects of the Lithuanian language and the standart language. When the Slavic formants of the borrowed individual words are phonetically adapted, analogous hybrid derivatives with that formant are already available in Lithuanian slang. There are few adverbs derived from the singular nominative case. There are also few adverbs derived from the conjugation forms of the verb, mostly the forms are borrowed from informal Russian language. There are a number of borrowed adverbs without formants: there are mostly phonetically, graphically adapted borrowings from Russian language; borrowings of this type are rare from English and German languages. The analysis found that the adaptive features of the slang adverbs are determined by systemic regularities. Slang adverbs are adapted phonetically and graphically according to the principle of substitution of foreign phonemes as close as possible to their own. Slang lexicon tends to copy standart language models and integrate into Lithuanian language derivative and flexic paradigms.

ABREVIASI DALAM PERCAKAPAN SEHARI-HARI DI MEDIA SOSIAL: SUATU KAJIAN MORFOLOGI

This article aims to describe a process of abbreviation occurred in the daily conversation through social media. The method used in this research is qualitative method. The data was taken from daily conversation through social media, such as line, whats app, facebook, twitter, and Instagram. The theory is that there is an abbreviation process and slang language in the conversation. The main main of this research is to describe the general features and the abbreviation processes used by the younger generation and the older generation from the Indonesian social media community. The result shows that there are three abbreviation processes, namely acronym, shortening, and word fragmentation. Among the three, acronym is the most common. The reason for this is that the speakers are intended to safe time, fill in the gap f communication, and to show social strata. Keywords: abbreviation, morphology, social media

Sentimental Analysis based on Text and Emoticons

Long range informal communication has bit by bit become an everyday practice for individuals to post their feelings, perspectives and remarks on any item or individual. Individuals share their sentiments online in an exceptionally casual language. Along these lines, it is exceptionally troublesome errand to break down precise slants connected with that common language. Estimation Analysis is an investigation of individuals' mentality, assessments, and feelings to arrange whether it is certain, negative or impartial. Utilization of emojis via web-based networking media has expanded quickly as of late. Subsequently, we have concentrated more on how emojis assume a significant job in opinion examination. Different elements that influence supposition examination are talked about quickly in this paper. Likewise, different issues like mockery identification, multilingualism, taking care of abbreviations and slang language, lexical variety and dynamic word reference dealing with are talked about. A few examinations were then performed to break down the impact of emojis on tweets utilizing AI strategies. At long last, the general execution investigation of the classifier was figured utilizing the NLTK stemming system.

Perubahan Fonologis Bahasa Gaul dalam Percakapan Whatsapp Kelompok Siswa Kelas 9 MTs Muhammadiyah 05 Kemusu

The study entitled "Phonological Changes in Slang in Conversations Whatsapp Groups of 9th Grade Students Muhammadiyah 05 Kemusu” aims to analyze the field of phonological studies in slang. This study uses a descriptive qualitative method that produces descriptive data in the form of written or oral words from people and observable behavior. The data source of this research is the form of Whatsapp group chat. The object in this study is slang. The subjects chosen are 9th grade students of MTs Muhammadiyah 05 Kemusu. The data analysis technique uses data reduction, data presentation, and verification. The results of the study show that (a) there is a form of slang; (b) there are 50 forms of slang which are transcribed in phonetic and phonemic forms; (c) there are 9 phonological traits in slang which are classified based on changes in consonant phoneme, vocal, mixed, combination of phonemes, anaphylaxis or phoneme addition, contraction or shortening, acronym, metathesis, and diphthongized or semivowel; and (d) the causes of the use of slang language are friendship, prestige, age, gender, and technological development

SLANG LANGUAGE ANALYSIS OF THE WARIA COMMUNITY IN THE KEMBANG KUNING BURNING COMPLEX, SURABAYA

This study discusses the form of words, the meaning of words and changes in the phonological structure of words in slang in the transgender community in the Kembang Kuning Surabaya Cemetery. The objectives of this study are (1) to describe the form of words in slang for the transgender community in the Kembang Kuning Surabaya Cemetery, (2) to describe the meaning of words in slang for the transgender community in the Kembang Kuning Surabaya Cemetery, and (3) to describe the structural changes. phonology of words in slang in the transgender community in the Kembang Kuning Surabaya Cemetery. This type of research is a qualitative descriptive study. Sources of data taken by researchers are the slang language of the transgender community in the Kembang Kuning Surabaya Cemetery, with data in the form of slang words. The total data total is 71 data. The data collection method used the observation method and used basic techniques, namely tapping techniques, while the advanced techniques used the listening technique without proficient involvement, the listening technique involved proficiently, the recording technique and the note taking technique. The data analysis method used the split method and used the basic technique, namely the direct element technique, while the advanced technique used the replacement technique and used the equivalent method and used the basic technique, namely the determining element sorting technique (PUP) while the advanced technique used the differentiating linking technique (HBB). The results of the study are as follows. First, the researcher found the form of the slang word namely; (1) basic words, (2) affix words, (3) rephrases, and (4) compound words. Second, the researcher found the meaning of slang words, namely; (1) denotative, and (2) connotative. Third, the researcher found changes in the phonological structure of slang words, namely; (1) zeroization, (2) anaptixis, and (3) metathysis.

AN ANALYSIS OF SLANG LANGUAGE TYPES IN "THE DUFF" MOVIE"

The purpose of this study was to determine the type of use of slang language. The types of slangs were analyzed from “The Duff” movie. Theories used in this research were Paltridge (2004) about the types of slang language and Yanhong (2013) about the functions of slang. This study was conducted by using a descriptive qualitative method. The data was from a movie entitled “The Duff” in the year of 2015 with genre of teen or comedy. After conducting the analysis, this study showed that there were six slang languages from eleven type of slang language. They were society, cockney, workmen’s, public school and university, public house, and theater. Society was found as the most dominant slang language with society with 41 data (54,6%), public house with 20 data (26,6%), workmen’s with 6 data (8 %), cockney with 5 data (6,6%), theater with 2 data(2,6%), and public school and university with 1 data (1,3%). Finally, the researcher hopes that this study will be useful for the future improvement of studying literature, especially about slang language

AN ANALYSIS SLANG LANGUAGE FOUND IN KELUARGA BESAR GROUP WHATSAPP

This study was conducted to examine the types of slang words and the meaning of slang language used by members of Keluarga Besar group whatsapp. This research includes morphology and phonology. To find out types of slang in terms of phonology, Chrystal (1992) was employed to analyze the data. The researcher also used theory from Harimurti Kridaksana (1989) to analysis the tetun slang language in terms of morphology. From the analysis 17th September until 17th November 2019 used the theory from crystal (1992), it was found that from four types of slang languages in terms of phonology, there are three kinds of slang languages applied by the members of Keluarga Besar group whatsapp. 115 times of occurrence was found in their conversations, divided into: addition (15), replacement (2), sound transposition divided as: word reversal (80), PVB (14), insert one letter (2), sound change (2). While for morphology process,it was found that from four types of slang words in terms of morphology, there are two kinds of slang language applied by the members of Keluarga Besar group whatsapp. 13 times of occurrence was found in their conversations divided into: prefix (11), infix (1), and reduplication (2).

Image Spam Detection Using Machine Learning and Natural Language Processing

Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

Modelling Abbreviation in Internet Slang: a Comparison Study of Indonesian Internet Slang and English Internet Slang

People, all around the world, use internet recently. The usage of internet changes the language usage as well. People tend to use short word thus they are abbreviating some words or phrases. Many of new words are coined lately because of the using of internet. The formation of the new word then belongs to the study of morphology. The main theory of this research is morphology. Moreover, it uses the theory of abbreviation. Some types of abbreviation such as blends, acronym, alphabetism and clipping are found in this research.This research entitled ‘Modelling Abbreviation in Internet Slang: A Comparison Study of Indonesian Internet Slang and English Internet Slang’. The data are taken from some websites which have internet slang dictionary. The writer only took 20 data for this research from those online internet slang dictionaries. There are ten (10) data for English Internet slang language and ten (10) data for Indonesian internet slang language. Conducting the analysis, the writer revealed that the process of abbreviation in English internet slang language is as follow: Blends (1): swexy; Acronym (2): Afair, Mapoti; Alphabetism (2): BTW, FYI; and Clipping (2): acc, app. Meanwhile, the result of abbreviation process in Indonesian internet slang language is as follow: Blends (3): gaptek, jones, palbis; Acronym (3): bais, ucul, woles; Alphabetism (2): PHP, SMS; and Clipping (2): krik, cus.

Slang Language Subtitle Strategy in the Movie Entitled “The Social Network”

This paper focuses on the analysis of translation strategies of slang language as employed in the movie entitled “The Social Network”. This paper analyzes the translation strategies of the slang language from English into Indonesia from the movie entitled “The Social Network”. Baker’s translation strategies were used in this paper in order to analyze the data of this paper. The study findings reveal that there are 30 slang words used in this movie, and the subtitler adopted five translation strategies by Baker for translating the slang words in this movie. The analysis also reveals that most of the slang words in the movie have related words with the target language. There are no English slang words that were translated into Indonesia slang words in the movie. The subtitler mostly used the strategies for translating the English slang words into Indonesian words that have a similar expressive meaning. It can be interpreted as a result of the different culture of both countries that makes different slang words.

THE PHENOMENON OF SLANG LANGUAGE ON FACEBOOK AND TWITTER

This paper describes about the phenomenon of slang language on social network, namelyfacebook and twitter. Slang in this paper belongs to sociolinguistics study, especially sociolect. This isa descriptive qualitative method, which consists of three phase: data collection, data analysis, andpresentation of the result of data analysis. The data were collected by observing the netters utterance.Data were analysed through two phases. First, analyzing the meaning of the slang. Second, wasanalysing the categories of the slang.There are four point findings in this study. Firstly, the various forms of slang expression unitsare words and phrases. Secondly, the variety of linguistic processes forming units slang expression areby adding sound, erasing sound, changing vocal, replacing consonant, shifting sound, returning sound,repeating, and changing spell. Thirdly, the source languages of various slang reference units are fromnational language, regional languages, and foreign languages. Fourthly, the communicative functionsof various units of slang expressions are informative function, directive function, expressive function,komisif function, phatic function, and poetic function. Finally, from the finding of those four points inthis study, we know that slang language on facebook and twitter has its own uniqueness compared toother types of language.

Metaphorical Potential of Slang Language: some Results of the Pilot Experiment

This article is devoted to the study of the slang language of youth on the basis of the results of a pilot free associative experiment. The purpose of this study is to identify the content of the stereotypical notions for native speakers, and to reveal the links that exist in the conceptual system of native speakers. In this regard, a pilot associative experiment was conducted among 40 students, belonging to the youth, who are either native English speakers or who speak English fluently. There were also presented the results of a free associative experiment aimed at identifying components that reflect national and individual characteristics of the perception of slangism, as well as the metaphorical characteristics of naive discourse, which are universal for young people. In addition, we were able to identify the influence of extra-linguistic factors on the content of the associative thesaurus.

Modelling Abbreviation In Internet Slang: a Comparison Study of Indonesian Internet Slang and English Internet Slang

People, all around the world, use internet recently. The usage of internet changes the language usage as well. People tend to use short word thus they are abbreviating some words or phrases. Many of new words are coined lately because of the using of internet. The formation of the new word then belongs to the study of morphology. The main theory of this research is morphology. Moreover, it uses the theory of abbreviation. Some types of abbreviation such as blends, acronym, alphabetism and clipping are found in this research.This research entitled ‘Modelling Abbreviation in Internet Slang: A Comparison Study of Indonesian Internet Slang and English Internet Slang’. The data are taken from some websites which have internet slang dictionary. The writer only took 20 data for this research from those online internet slang dictionaries. There are ten (10) data for English Internet slang language and ten (10) data for Indonesian internet slang language. Conducting the analysis, the writer revealed that the process of abbreviation in English internet slang language is as follow: Blends (1): swexy; Acronym (2): Afair, Mapoti; Alphabetism (2): BTW, FYI; and Clipping (2): acc, app. Meanwhile, the result of abbreviation process in Indonesian internet slang language is as follow: Blends (3): gaptek, jones, palbis; Acronym (3): bais, ucul, woles; Alphabetism (2): PHP, SMS; and Clipping (2): krik, cus.

Semantics Analysis of Slang (SAOS) in Social Media of Millennial Generation

Generation were born in every era eventually create their own language not least the latest generation commonly called the millennial generation. Specifically, the aim of our work broadens current knowledge of the phenomenon of Semantics Analysis of Slang (SAOS) in Social Media of Millennial Generation. This study investigated: 1) the types of SAOS used by millennial generation in social media; 2) how the meaning of SAOS used by millennial generation in social media; 3) why millennial generation used SAOS in social media. This study applied descriptive qualitative research in order to reveal the data from the discourse of English Department students at Universitas Islam Negeri Sumatera Utara in social media such as Facebook and Instagram and it was obtained from observation, interview and documentation. The data showed that millennial generation used slang language in their communication in social media namely 33 slang languages in Facebook and 31 slang languages in Instagram. These languages consisted of five types of slang; fresh and creative; flippant; imitative; acronym; and clipping. The millennial generation used SAOS in their social media because some reasons such as to address, to initiate relax conversation, to express impression and to show intimacy. Keywords: Slang, Types of slang, Reasons of slang, millennial generation

An Observational Study Of Verbal And Nonverbal Communication In Female And Male University Students


 Gender based sociological and linguistic studies show that differences exist in communication style of men and women, boys and girls. Although many gender stereotypes have changed with modernization and awareness about gender issues however common assumption is that ‘girls are talkative’ and boys are ‘less emotional’. In the context of transitions in culture and society, this study explored verbal and nonverbal communication differences among male and female university students. Using purposive sampling method, male and female students were observed in three types of groups sitting at different social spots in University of Karachi. In total 30 male and 30 female students were observed for verbal and nonverbal communication. The units of observations were Paralanguage (words, pitch, volume, speaking rate) for verbal communication and gestures, facial expressions, eye contact, distance and space when sitting and standing. Results showed that overall both boys and girls were talkative and used slang language within same sex groups. However, the topic of discussion was personal for girls and politics for boys. Females kept a space and distance while sitting and standing as compared to boys. Within groups, male and female student’s body language showed superiority. The study has limitations therefore generalization of results for other gender-typed situations are not promising. Future studies could examine in other situations where status and power difference exist within relationships.