! Identifying New Sentiment Slang through Orthographic Pleonasm Online : Yasss Slay Gorg Queen

Identifying neologisms is important for natural language processing of social web text when informal language is standard and youth slang is common. For example, failing to identify neologisms can reduce the accuracy of lexical sentiment analysis if opinions are frequently expressed in words that are too new to be in the sentiment dictionary. This article proposes a method based on orthographic pleonasm to identify emotion-related neologisms in the social web: finding words with the most different letter repetition spelling variations. For this method, non-dictionary words are extracted from a large social web corpus, spelling standardisation is applied, and then words are ranked in decreasing order of spelling variation frequency. Words with the most spelling variations are then KWIC-analysed for semantic context. Applied to a collection of comments on YouTube influencers, this method found neologisms like slay and early as positive terms, mixed with traditional sentiment words, exclamations, and nouns. Although orthographic pleonasm was originally used to express the speaker’s rhythm and one of voice, it is also used for initialisms in a way that is difficult to vocalise. The method is therefore a practical method to identify new sentiment slang, including both normal words and initialisms. NATURAL LANGUAGE PROCESSING (NLP) algorithms applied to social web texts can struggle with non-standard spellings and grammar, especially if they are optimised for formal documents or applied to texts that are rich in nonstandard features [1], [2]. Slang words are a problem because of this and because they vary between subgroups in society and evolve over time [3]. Since new slang words periodically appear and are extensively used by young people, IEEE Intelligent Systems 36(4) This article is part of the IEEE IS ACSA series c © 2021 ACSA Affective Computing and Sentiment Analysis NLP algorithms need to be able to be updated for these terms to perform optimally. For example, sentiment analysis algorithms [4] may need to exploit slang [5] and may be fooled when new sentiment slang terms become the norm amongst some social groups [6], such as (in the past) dad, lush skill, tidy, and wicked. Thus, a method is needed to identify new sentiment slang. Although slang is sometimes manually identified [7], an automatic or semi-automatic method might be more efficient. In practical terms is possible to consult slang dictionaries [8], [9], [10], [6], but these can be large, of uncertain provenance and general purpose rather than tailored for sentiment. Similarly, slang-based extensions to linguistic resources like WordNet [11] may become dated. Lexicographic techniques to identify new words rely on frequency to find them, such as by identifying the most common non-dictionary words found in a social web corpus. Whilst this works for neologisms like lol, it will not work for existing words repurposed as slang, including the examples above. Topic modelling can be used to check if a word has changed meaning, potentially with a new slang meaning, relative to changes in discussion topics (experiment 2 of: [12]). This method does not identify new words, however, and is not specific to sentiment. Deep learning has been used to identify dictionary sentences with slang from Wall Street news sentences, finding that slang commonly associates with syntactic changes or shifts [13]. This method is promising but may not work well on informally structured language. Slang words not in a dictionary can also be identified through web context information, but this does not work for slang meanings of standard words [14]. Sentiment words can be also extracted from dialect language, including both slang and standard terms [15], although this is a limited context. With a similarly restricted context, polarised creative political slang can be found by comparing word frequencies between sets of comments from different political positions, after removing dictionary words, proper nouns and known slang [16]. Rhyming slang has special detection algorithms [17] but is a rare type. Finally, a semi-automatic method has also been devised to create an annotated corpus of youth slang [18]. In summary, whilst many methods exist to detect slang or, in limited contexts, sentiment slang, none detect new sentiment slang in a general context. This article harnesses a relatively unique style of informal social web text to detect new sentiment terms. Computer-mediated communication encourages the development of styles that adapt to the medium. Informal conversational messages may be misinterpreted when written because they lack the visual cues that are necessary to interpret them. This has led to devices like emoticons to either inject sentiment or indicate the type of sentiment intended by the author [19]. The emoticon therefore substitutes for the ability to read the speaker’s expression. A second device is to spell words in the way that they are spoken, injecting extra letters to indicate emphasis or enthusiasm, sometimes called “vocal spelling” [20], [21]. It is orthographic pleonasm through the inclusion of (relatively) redundant letters. This substitutes for the ability to interpret a person’s tone of voice or intonation. It can also be used to inject a regional accent into words [22]. Both emoticons and spelling alterations seem to be widely enough used to be easily decoded by the reader. Capitalisation, underscores, asterisks and brackets can also be used for emphasis or to suggest increased volume of speech [23]. Whilst emoticons and altered spelling have been used in sentiment analysis as indicators of sentiment [24], [25], they have not been used to detect slang words. This article proposes a new method to identify new sentiment slang from a set of social web texts by exploiting orthographic pleonasm: redundant letters added to a word. For example, someone might comment wickiiiiiiid on Facebook, with the redundant letters functioning to emphasise the sentiment of the word. The intuitions behind using orthographic pleonasm to identify new sentiment-related words are that (a) slang spelling is likely to associate with slang words because both are types of informal language, so slang words seem likely to be written with extensive orthographic pleonasm, and (b) emotional states can lead to changed tones of voice, so vocal spelling can be expected for sentiment words. Although there are other creative spelling techniques than repeating letters, such as leet (e.g., [26]), orthographic pleonasm seems to have the most IEEE Intelligent Systems 36(4) natural association with sentiment. The orthographic pleonasm semi-automatic method to detect sentiment slang has four stages and requires a collection of contemporary social web texts. 1) Extract all words from a set of social web texts without any spell checking. 2) Convert each word into a common standard form by deleting repeated letters until a dictionary word is found or all doubled letters have been removed (e.g., lushhhh to lush). This is the shortened form of the word. 3) Count the number of spelling variations of each shortened word form, and rank the short word forms in descending order of this number. 4) Manually check the top N words for meaning using the Key Word In Context KWIC method to identify the sentiment slang. EXPERIMENT The orthographic pleonasm method was applied to a set of nine million YouTube comments on the videos of 223 UK lifestyle-based female influencers. These were downloaded in November-December 2020 with the YouTube API through the free software Mozdeh. A set from the same country as the author is helpful for slang identification and lifestyle influencer comments are particularly informal and sentiment rich, so are a suitable source. They have the additional practical benefit that they are easy to collect, seem to be relatively spam-free, and are date-stamped. Individual YouTube commenters can sometimes be prolific, however, which may skew the results towards the writing styles of a few people. To guard against this, each commenter was limited to one comment per month, using a random number generator (in Mozdeh). This is a compromise that gives a moderate amount of extra weight to prolific commenters. To further guard against domination by individuals, commenters most using each word were checked during the follow-up KWIC process (again, an option in Mozdeh), for being the single dominant users of the term. No causes for concern were discovered in this regard. This stage produced 13,264,344 comments from July 2007 to December 2020. The orthographic pleonasm method was applied, as described above, and the top 100 terms were manually checked by the author for slang uses using the KWIC approach (using Mozdeh’s concordancer) with the original YouTube comments. Each word was also classified for primary function to give supporting contextual information about orthographic pleonasm in practice. RESULTS Thirty-six of the 100 words examined were common sentiment terms, with the extra spelling serving to boost the strength of the sentiment expressed. There were many laugh variants (hahahahahahahaha, hahaha, hahahahahahaha, hahahahaha, hahahaha, hahahahahaha, haha) and two abbreviations for laughter (lmao, lol). Whilst none of these are traditional sentiment terms, they seem to have a long online history. There were also positive opinion words (love, amazing, beautiful, loved, cute, gorgeous, pretty, awesome, stunning, super, perfect, best, great, nice, congrats, lurve, crazy). Several non-sentiment words expressed a positive sentiment in their typical contexts. These included (waited for) long, (waited for) ages, finally (it is ready), (cant) wait, (love) it, (want) more, (this is) huge. Two also expressed emotions: happy, crying. The latter word was expressed typically in positive contexts like weddings and after seeing a new pet or flat. Many different numbers of kisses (x) were also sent. The remainin