Set of all Unicode and theirs representation texts. With open(slang_filename,'rb') as exRtFile:ĮxchReader = csv.reader(exRtFile,delimiter='`',quoting=csv.QUOTE_NONE)Īnother dataset which might be useful if you working in similar domain, Here is a small Python snippet to use the dataset. Moreover, I would request any reader if they find any new slang, add it here and share it. You might find many slangs missing in here.Īlso, many of the slangs/acronyms are region or cluster specific (used primarily only by certain groups of people) which are pretty difficult to capture. If a Slang has multiple meanings, each is divided by ‘|’ Symbol.Īlthough the repository contains 7500+ entries, it’s still only the tip of the iceberg. PS: Didn’t use any of the ‘commonly’ used delimiter characters because they are ‘commonly’ used for ASCII emoticons and expressions.ĭataset contains two Rows: Slang and its Meaning This post is to share that set with the Internet, so it might be useful for all those who are exploring this field just like yours truly. Hence we created a dataset of 7500+ Slag words and meanings from scrapping. Or is a good repository, but not very extensive one. There are many online web resources for the same (not many in dataset format), like the one we found most useful: Unfortunately, we didn’t come across any extensive list, so we decided to create one. Hence we searched through the internet to get a reliable dataset for these slangs and acronyms (slang dictionary or texting dictionary). The OEDs earliest citation of this slang term for the police is from a 1983. To predict an emotion one must understand it first. According to the Oxford English Dictionary (OED), it first appeared in 1978. 2MORO, ALAP (As Long As Possible), PERF (Perfect),etc. his leads users to employ enormous use of slangs and acronyms in place of words or even the entire phrases.Į.g. One of the challenges we faced has been due to constraint of the Text length allowed for posts, which is only 140 characters. We’ve been considering multiple parameters for learning, like, context of the text, usage of Emoticons, emojis, special characters, hash Tags etc.
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We (my team & I) are building a Machine Learning model which can predict emotions based on data posted on micro-blog sites, like Twitter. Currently, I am working on a project on Emotion Detection in Text.