This classifier determines if a text is positive or negative. It is well suited for both short and long texts (tweets, Facebook statuses, blog posts, product reviews etc). It’s trained on 2.8 million documents with data from Twitter, Amazon product reviews and movie reviews. It can be used to conduct research, brand surveys and see trends around market campaigns. You may access the sentiment analysis api by signing up (free)! Read more about the classifier in at blog.uclassify.com/sentiment-analysis-api
by uClassifyThe IAB Taxonomy V2 classifier categories texts into 560 topics. It has four levels of depth, a main category (e.g. sports, business or science) and subcategories (soccer, agriculture or physics). It's based on the IAB Quality Assurance Guidelines (QAG) Content Taxonomy V2, released 1 March 2017. The class name has up to 6 parts, separated by underscores: "main topic_sub topic_id1_id2_id3_id4" where the ids are the IAB level ids (use the ids for mapping).
by uClassifyCategories a text into a topic (Arts, Business, Computers, Games, Health, Home, Recreation, Science, Society and Sports). Each of those topics has more specific child classifier (Art Topics, Business Topics etc). It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyThis classifier tries to figure out if a text is written by a male or female. It has been trained on 11000 blogs (5500 blogs written by females and 5500 by males). More text gives better results.
by uClassify
We recommend using our new better classifier called 'Language Detector' instead. Classifies the language of a text by looking on about 4000 commonly used words per language. It works best with clean texts but can also be used for HTML pages. For reliable results HTML pages need more text content (since HTML often contains English words and comments).
by uClassify
The IAB Taxonomy classifier categories texts into one of 360 topics. It has two levels of depth, a main category (e.g. sports, business or science) and subcategories (soccer, agriculture or physics). It's based on the IAB Quality Assurance Guidelines (QAG) Taxonomy. The class name has 4 parts, separated by underscores: "main topic_sub topic_id1_id2" where the ids are the IAB level 1 and 2 ids.
by uClassifyThis classifier tries to estimate to which age group a blog belongs. The training data is based upon about 7000 blogs collected randomly from Internet.
by uClassifyThe state of mind of the writer - upset or happy. On the extreme side there is angry, hateful writers and ton the other extreme there is joyful and loving writers. The measured accuracy is 96% (using 10-fold cross validation). For reliable results we recommend that you use at least 200 words.
by prfektAnalyzes the Extraversion/Introversion dimension of the personality type according to Myers-Briggs personality model. The analysis is based on the writing style and should NOT be confused with the MBTI (c) which determines personality type based on self-assessment questionnaires. Training texts are manually selected from mainly blog posts based on the authors understanding of personality and writing style (see Jensen & DiTiberio, 1989).
by prfektDetermines the Thinking/Feeling dimension of the personality type according to Myers-Briggs personality model. The analysis is based on the writing style and should NOT be confused with the MBTI (c) which determines personality type based on self-assessment questionnaires. Training texts are manually selected mainly from blog posts based on the authors understanding of personality and writing style (see Jensen & DiTiberio, 1989).
by prfektDetermines the Judging/Perceiving dimension of the personality type according to Myers-Briggs personality model. The analysis is based on the writing style and should NOT be confused with the MBTI (c) which determines personality type based on self-assessment questionnaires. Training texts are manually selected mainly from blog posts based on the authors understanding of personality and writing style (see Jensen & DiTiberio, 1989).
by prfektDetermines the Sensing/iNtuition dimension of the personality type according to Myers-Briggs personality model. The analysis is based on the writing style and should NOT be confused with the MBTI (c) which determines personality type based on self-assessment questionnaires. Training texts are manually selected mainly from blog posts based on the authors understanding of personality and writing style (see Jensen & DiTiberio, 1989).
by prfektDetermines the tonality of a text - corporate (formal) or personal (informal). Helps distinguish between prosumer media and pro media for instance.
by prfektThe IAB Content Taxonomy V3 classifier categorizes text into 617 topics. It has a main category (e.g. sports, business or science) and a leaf category (soccer, agriculture or physics). The class name has 3 parts, separated by underscores: "main topic_sub topic_uid" where the uid is the category's official IAB unique id. This text classifier is based on the IAB Quality Assurance Guidelines (QAG) Content Taxonomy V3, released in September 2021.
by uClassifyCategories a text into a computer topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a society topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a sport topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into an art topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into an business topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassify
This classifier identifies the language of a text. It can detect more than 370 major and rare languages; living (e.g. English, Chinese), constructed (e.g. Klingon, Esperanto), ancient and extinct. At least a few words are needed to get accurate results. The language name is appended with an underscore followed by its ISO 639-3 code. The text is expected to be UTF-8 encoded, also keep in mind that more languages may be added.
by uClassifyCategories a text into a recreation topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassify
Tries to determine whether a text was written by someone who is liberal or conservative in ideology.
by PolitimindCategories a text into a home topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a health topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassifyCategories a text into a science topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassify
This classifier categorizes news articles. It was trained with 1000 hand picked articles from major news sources per category. The text used was a combination of each article's title, description and scraped and cleaned text.
by mvazquez
Latvian sentiment classifier. Trained on food tweet data from www.twitediens.ml
by saiferCategories a text into a game topic. Use the parent classifier 'Topics' to find out if a text belongs in this category. It uses a subset of topics from the Open Directory Project at http://www.dmoz.org.
by uClassify
This classifier has been trained on 21 different classical authors. We have used about three books per author collected from the Gutenberg project. It only works for English texts. Try it out to see which poet your blog or text is most alike!
by uClassify
Canadian government publication category classifier. categories map = { "Agriculture, environment, fisheries and natural resources": "agriculture_environment", "Arts, culture and entertainment": "arts_culture", "Business, industry and trade": "business_industry", "Economics and finance": "economics_finance", "Education, language and training": "education_language", "Employment and labour": "employment_labour", "Government, Parliament and politics": "government_politics", "Health and safety": "health_safety", "Indigenous affairs": "indigenous_affairs", "Information and communications": "information_communications", "International affairs and defence": "international_affairs", "Law, justice and rights": "law_justice", "Science and technology": "science_technology", "Social affairs and population": "social_affairs", }
by Frederick
Analyzes the cognitive functions used for a text according to the Myers-Briggs Personality Theory. A database with more than 20 thousand words combining the slang words, words and phrase constructions most used by each type of personality, obtained in forums and controlled blogs. Please keep in mind that this test does not serve as an MBTI personality test, most people fluctuate between two or more types depending on the situation or subject, people can change mental state, for example, an INTP personality type talking about the past can be typed as a type of personality that uses the cognitive function Si, since this test only evaluates the functions used in the text. In order to get an idea of what an individual's personality is, it is advisable to gather a considerable amount of text (500 to 1000 words for a decent analysis - 9000 for perfect) written by that person in order to discover their most used functions. Usually, you'd get a better result if you get the person to answer an open-ended question where they express an opinion. Constantly updating for more accurate results. Enjoy! (Note: It only works well with native English speaking people)
by g4mes543Tries to determine the values (i.e. worldview) expressed by the author according to Clare W. "Graves Emergent Cyclic Levels of Existence Theory".
by prfekt
Classification between Semantic/pragmatic puns and phonological puns
by aangtce
by arturgorczynski
by virus32
Web Categories
by ephraimalbaro
by casper
Direction of focus - introversion/inner world (AQAL: UL + LR) or extraversion/outer world (AQAL: LL + UR). I believe this also shows wether a person is inner directed and thus prioritize other people´s opinions and experiences over one´s own (extravert) or outer directed (introvert). The class "extraversion" has been trained on 130455 features (words) whereof 24576 are unique.The class "introversion" has been trained on 83764 features (words) whereof 18111 are unique.
by prfekt
Tries to guess whether a statement is true or false.
by Politimind
Language style abstract (big words) or concrete (facts and details). AQAL: UR + UR vs LL + LR. The class "abstract" has been trained on 110178 features (words) whereof 22070 are unique. The class "concrete" has been trained on 104040 features (words) whereof 20693 are unique.
by prfekt
by amnorvend
Focusing purely on core or motivational drives of the person from each individual type alone, erasing any bit of archetypes or typically perceived common traits possible. Seeking a true objective enneagram type. Keep in mind, depending on the situation, subject matter, state of mind, emotion results could differ so keep it considerable and clear with around 2000+ for best accuracy usually open-ended, Biography, opinions, or something similar. (Disclaimer: This is not an enneagram test and don't define but a possibility analyzes enneagram and wing in order, updates time from time for better results and this shouldn't correlate or reconsider MBTI or any alternative type system) --- Examples of a few common stereotypes or traits being cut. 1: Ideal, logical, cold, strict, organized 2: Sociable, friendly, emotional, pleaser 3: Extroverted, charming, sociable, arrogant 4: emotional, introvert, creative, artistic, different 5: logical, intelligent, introvert, abstract, withdrawn 6: loyal, anxious, conventional, defenceless 7: Spontaneous, extrovert, adventurous carefree 8: Demanding, cold, controlling, extrovert 9: Passive, lazy, friendly, defenceless ---- Clarity of cores 1: Want to be internally moral, search the best option 2: Want to be loved, search criteria to be cared for 3: Want significance, special and importance 4: Want an identity/destiny to be satisfied with 5: Wants to be prepared for the environment’s criteria 6: Want clear direction, moment stability 7: Want comfort escape prolong suffering 8: Wants security of maintaining their life in control 9: Wants self/external peace with things kept
by ohubobubo
IAB Level One Classification with a Denver, Colorado twist. UPPER CASE text only, please.
by scox1000
Determines the perspective (i.e. quadrant) expressed by the author according to Ken Wilber's Integral Theory (AQAL - all quadrants all levels).
by prfekt
by umesh_menon13
Discourse denotes written and spoken communications, it can classify phrases into questions, answers or more fine grained categories such as agreement, disagreement, elaboration etc. It works best with short texts such as tweets. For longer texts consider splitting it into sentences or phrases on beforehand. It's based on the dataset from the paper "Characterizing Online Discussion Using Coarse Discourse Sequences (ICWSM '17)"
by uClassify
by DullDemoon3
Eggo
by boattyman
This news categorizer is only a simple example used in a tutorial. It has been trained on 20 texts per category (sports, entertainment and science) so don't expect too much (even though it seems to do incredibly well).
by uClassify
by ranez
This is a classifier for identifying web page contents.....
by cbaproject076
Mood Tracker is used best on individual blog posts, tweets, etc. which are then compared to other posts thus tracking the authors mood for that period!
by becky411
The monkeys playing on the airplane, together move to an island far away. They come from and to where?
by Chickyky
by someperson101
by FunctionXu
by Willothy
Okay, this classifier is still harboring much room for improvement. It's been trained on words across various astrology blogs. I hope you'll at least find it somewhat enjoyable!
by DullDemoon3
kvista's librarything classifier
by kvista
What parts of reality the author chooses to focus on. AQAL: quadrants. The class "Personal UL" has been trained on 27202 features (words) whereof 7384 are unique. The class "Philosophical LR" has been trained on 56561 features (words) whereof 13686 are unique. The class "Practical UR" has been trained on 76606 features (words) whereof 16798 are unique. The class "Social LL" has been trained on 53617 features (words) whereof 12721 are unique.
by prfekt
Interest in people (subjective) or things (objective). AQAL: UL + LL vs UR + LR. The class "people" has been trained on 80819 features (words) whereof 16949 are unique. The class "things" has totaly been trained on 133399 features (words) whereof 25393 are unique.
by prfekt
by fabfre
A classifier based on the Runic poetry and the poetic meaning of the Runes, rather than the modern-day interpretations.
by damir666
Classifies text into Symptoms, Cure and Diseases
by Talha
by P
by DullDemoon3
by scox1000
by angakokpanipaq
by damir666
danganronpa v3 characters! trained w quotes, a wip
by junko
by ming_0713
there are three types of websites in the world.
by bethglass
by BrickleRex
Classifies Hindustani Raags based on input. Input must be in the form of single-character Swars. EG: s R r G g m M p D d N n All Komal or Teevra Swars are capitalized. Input example: srg pds sdp grs Important: The Swars should be grouped in three-characters.
by Soham Korade
This classifier will try to predict if a movie passes or fails the Bechdel Test by looking at the plot and/or subtitles of a movie. It's is trained with about 6000 IMDB plots and 2400 movie subtitles that has failed and passed the Bechdel Test according to bechdeltest.com.
by uClassify
by multisystem
To tell the difference between Apple the Fruit and Apple Computer
by rcheramy
by Nelson
by nagendra
by Lincy Lougine
by khodrog
Classifies music into Rock or Pop based on lyrics. A few hundies of lyrics were used to train classifier. Ovaj klasifikator ce razvrstati muziku po žanrovima na osnovu teksta. Uspešnost zagarantovana!
by fon
by Danielmagox
Detects dad jokes Trained for non-dad-jokes with the help of a Slack server for a video game guild I was in and the Wikipedia article on shaped charges. May detect puns as dad-jokes. Other Joke category may be unreliable.
by DominicD
by achintyagi
Sentiments Analyzer for Hotel reviews
by Priya Parameswaran
by mansooranis
by balkis
Hotel stay sentiment
by pramodvj
by Mansour Omar Almenhali
by adauris
Sentiment analyzer model
by adwaithrm
by camilo2525
by 17pmg
by Anandvz
Detects if a script is a Roblox virus or not.
by Aon
classifies categories
by raine
test classifier
by eric234
Presenting a sentimental analyser - GoodGuy
by ardrasd
This project is based on hotel stay customer review and it is created for ML challenge.
by nikhilthomas112
by TylerBanducci
by pgaldamez
classifies priority
by raine
Krakin't Research and Development classifier. Please use this classifier to evaluate ideas and whether they are suitable for the Krakin't project. This way, no text will be collected, and no ideas stolen.
by krakint
by JBatz538
by RAOUYA AAMIR
by virus32
Text-based sentiment analyzer for hotel stay recommendations, created with few datasets from public review comments.
by sh3hz
by mesadhan
by gunawan01
by BrendaYY
by emoji
by Mez05
Klasifikasi ini dibuat agar dapat mengetahui zodiak berdasarkan tanggal dan bulan kelahiran.
by gracialubis
This classifier is a test to see if background information can be distinguished from recommendations.
by wmp0
Bu metin siniflandirma filtresi bilgisayar algoritmalari kullanarak bir yazinin hangi yazar tarafindan yazildigini tahmin etmeye çalisir. Sistem su an beta asamada ve ilk etapata kayitli 4 yazar var: Ahmet Turan Alkan,Cüneyt Özdemir, Cengiz Çandar ve Ezgi Basaran. Bu yazarlardan birinin yazisindan birkaç paragrafi kopyalayip asagidaki metin kutusuna yapistirin, uClassfiy! tusuna basinca sistem size yazinin hangi yazara ait olduguyla ilgili tahminini sunacak. Hüseyin Demirtas, boun, cogsci huseyindemirtas.net
by dilsayar
SentimentAnalyzer
by muhammedmt
Classifier for sports news stories with intelligence specific to Denver, CO. Uppercase text only.
by scox1000
sentiment analysis system_by_shebinkr_mukesh_Ajins
by shebinkr
by adauris2
by codeca
by gunawan01
by arturgorczynski
The Movie Theater Review Analyzer is a text classification model designed to categorize customer feedback about movie theaters into meaningful groups such as Positive Experiences, Complaints, and Suggestions. By analyzing written reviews, the system helps identify trends in customer satisfaction, operational issues, and potential improvements. This tool can assist theater management in prioritizing maintenance, enhancing service quality, and improving overall customer experience.
by chris_van0342
by renjithpta
Sentimental Analysis
by subish
by Keith Lucas
Monalisa Text classifier
by jairribeiro1
Typical info.nl topics
by oebe
by ashrita
by Feffo96
For The Dream Team
by JarJarBeatU
This is a collection of anime recommendations that are the best in my opinion according to their genre
by olsen hildan
ini klasifikasi minuman
by aisasalsabila
by pkc_2312
by Liadebersi
by maccal4245
by azizah jasmine
CustomerSentimentsModel-Ratheesh-SunTec
by chaosemb84
by thezero
by achintyagi
by Callum1049
by kaihv87
The origin of food in the world is very diverse, this sometimes confuses some people about the country of origin of a food. This search will make it easier for you to get to know the country of origin of a food just by writing the name of the food.
by Riris Devina
by surajsharma
by CoachNoah
by naj mohamed
It's only for russian texts! This classifier specifies whether the text is a work of science fiction or not. (Roger Zelazny detected with unstable results, for obvious reasons.) And of course the Russian text in the description of the classifier is not supported by uClassify...
by Shadowmaster
This classifier currently outputs 2 categories..
by achintyagi
by Qilaami
by Elijah W
This classifier is only trained on offensive vs inoffensive instances of the word "mother" in Egyptian Arabic. Done for a graduation project.
by yousraghawi
Intent identifier
by Guren22
by laine
This classifier determines if a movie or series is recommended or not recommended
by friska
by Timoverkade
by 12704725
This is a Test Classifier.
by Srikanth Tanniru
by ssdupree
by lanne5454
by Riris Devina
Kalsifikasi ini mencoba memperkirakan jenis penyakit tertentu termasuk Penyakit Turunan atau Tidak Turunan dengan 192 data penyakit yang ada di Indonesia. Data penyakit dikumpul dari website terpercaya dan akan terus di upgrade perkembangannya.
by riyahchoiadha
Describe a fruit that is listed and watch the Ai work it’s magic!
by Sophia_R_S
Modelo para clasificar documentos por tipo de archivo y extensión
by safe
by muchskeptical
Penyakit virus corona (COVID-19) adalah penyakit menular yang disebabkan oleh virus SARS-CoV-2. Sebagian besar orang yang tertular COVID-19 akan mengalami gejala ringan hingga sedang, dan akan pulih tanpa penanganan khusus. Namun, sebagian orang akan mengalami sakit parah dan memerlukan bantuan medis.
by Fanny_123
identify whether the charge is a state, county, or federal tax
by pratadh
by PColinot
by Muammar_7
Analizador de textos spam en busca de indicios que demuestren si es spam. Tasa de aciertos: 92,3%. Solo para textos en español. Spam text analizer for signs that prove it´s spam. Hit rate: 92.3%. For spanish texts only.
by rugualler
by wahyu07
by Souleaterdoge
by budisitio
by gracialubis
Klasifikasi respon email ini bertujuan untuk mengklasifikasikan email berdasarkan dengan responnya, kategori email disini, adalah email formal, yang berisi lamaran/tawaran pekerjaan, dan email pekerjaan secara umum. Klasifikasi ini nantinya akan menampilkan persentase ketertarikan pada email tersebut, atau sebaliknya. Sumber data diperoleh dari sampel-sampel template email formal, dan beberapa klasifikasi kata yang relevan. Tersedia dalam Bahasa Indonesia dan Bahasa Inggris. || Contoh template email (interested/tertarik) : || Thank you for your invitation to interview for the [Job Title] position at [Company Name, e.g. Resume Worded]. I am available at [confirm the proposed date and time or suggest a few specific times you are available throughout the week] and am looking forward to meeting with [Hiring Manager] at [the company’s location or via Zoom if it’s a virtual interview]. || Terima kasih atas undangan wawancara untuk posisi [Jabatan] di [Nama Perusahaan, mis. Lanjutkan Kata]. Saya tersedia di [konfirmasi tanggal dan waktu yang diusulkan atau sarankan beberapa waktu tertentu Anda tersedia sepanjang minggu] dan saya menantikan untuk bertemu dengan [Manajer Perekrutan] di [lokasi perusahaan atau melalui Zoom jika itu adalah wawancara virtual]. || Contoh template email (not interested/tidak tertarik) : || Thank you for your invitation to interview for the [Job Title] position at [Company Name, e.g. Resume Worded]. Unfortunately, I have decided not to accept the position, as it isn’t a good fit for me at this time. || Terima kasih atas undangan wawancara untuk posisi [Jabatan] di [Nama Perusahaan, mis. Lanjutkan Kata]. Sayangnya, saya telah memutuskan untuk tidak menerima posisi tersebut, karena tidak cocok untuk saya saat ini.
by mahalliqr
by T3RRAFORM
by rankiner
hii
by olsen hildan
by BenCS
by JBatz538
This classifier probably has the equivalent usefulness of a buzzfeed quiz. I guess you might be able to use it to sorta quantify NPOV/encyclopedic tone. This classifier has been trained on 3231 total words so far.
by DominicD
by ANUmadan01
Digunakan untuk menentukan ataupun mengklasifikasikan mana makanan yang cepat saji dan mana yang tidak.
by budisitio
This is my first classifier. Type the name of some paradox pokemon and it will sort it into Pokemon Scarlet or Pokemon Violet.
by Koraidon_scarlet
by OwenS
Klasifikasi ini dibuat untuk mengelompokkan antara ilmu-ilmu eksakta dan non eksakta.
by aqilahsn
by cecgoncalves1
Varied Animals
by autumnpa
by Lidia Martins
안녕하세요 여로분
by riyahchoiadha
by JaniayaC
by samkm38
for ai class
by khawkins749
Classifies sentiments expressed in the comments made by customers of a hotel. The sentiments can be either positive, negative, or neutral.
by janardh
by Muammar_7
Penggunaan kata baku dan non baku dalam sebuah kalimat
by fit3aulia
Novel adalah jenis karya sastra berbentuk prosa yang di dalamnya memuat cerita berdasarkan pemikiran si penulis. Sebab menjadi salah satu jenis bacaan yang banyak diminati berbagai kalangan, ada banyak kumpulan novel terbaik 2022 yang bisa menjadi referensi.
by lulu_30
https://docs.google.com/document/d/14F501vSUN8PqJ_ZIAA4gfPUK5fbFVro4-ewJuIUmwCw/edit?usp=sharing
by calvinnho
by Daram
by microb12
1.Data dan Sumber Data yang valid Data mengenai daftar provinsi yang ada di Indonesia yang diklasifikasikan berdasarkan pulau yang ada di Indonesia. Hal ini dapat membantu mengklasifikasikan pulau yang ada di Indonesia berdasarkan provinsi yang ada. https://berita.99.co/34-provinsi-di-indonesia-dan-ibukotanya/ 2.Panduan agar Dapat Digunakan di UClassify Agar dapat mengetahui asal pulau dari Provinsi yang ada di Indonesia, maka pengguna harus terlebih dahulu menginput nama provinsi ke dalam kolom classify text. Apabila sesuai dengan nama provinsi yang ada di Indonesia, maka akan muncul asal pulau yang ada di Indonesia. namun bila tidak menginput nama provinsi yang ada di Indonesia, maka hasil pencarian asal pulau tersebut tidak akan muncul 3.Penerapan Penggunaan Produk di UClassify Agar dapat diterapkan dalam UClassify, maka pengguna perlu menginput kalimat dan nama provinsi yang ada di Indonesia agar dapat memunculkan pulau yang ada di Indonesia. Kemudian akan muncul hasil yang sesuai dengan pulau yang telah di diisikan. Contoh: Saya bertempat tinggal di Jawa Tengah Maka hasil yang akan muncul adalah: Jawa Tengah terletak di Pulau Jawa
by Samuelmartin
by maxLingenfelter
by dillzo
by henrychino95
http
by annisa1212
hotel experience - under test
by skumar
Clasifica los adjetivos como 'bien' o 'mal' de un texto en español, en las clases de colores 'rojo' o 'verde'.
by carlosPFG
by kristinamaretta
by Samuelmartin
by kendallpi
by moonat5128
Permite classificar um dispositivo informático, sendo este entrada, saída ou entrada/ saída.
by cristinasilvaaea
by Muhammad Rizki
Kata baku adalah kata yang sesuai KBBI
by putrishakirah
by Qilaami
by audreyclarissa
1. SUMBER DATA DAN PENJELASAN Chat Berbau Pornografi Di aplikasi MiChat diperoleh dari analasis para pegguna michat di google dan banyaknya kasus tentang Aplikasi MiChat yang menjadi sarana melakukan Prostitusi Online di Indonesia. Kata-kata banned dichat ini akan mengidentifikasi USER yang menyimpang dari penggunaaan MiChat yang seharusnya (baik). SUMBER DATA: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.bangtax.net%2F2018%2F12%2Fsisi-gelap-dari-aplikasi-michat.html&psig=AOvVaw0GPloap6Ua-i_t2_6DKhEZ&ust=1669380945651000&source=images&cd=vfe&ved=0CBEQjhxqFwoTCMiInuPuxvsCFQAAAAAdAAAAABAa https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.bangtax.net%2F2019%2F11%2Fkenyataan-dunia-kelam-aplikasi-michat-yang-kontroversial.html&psig=AOvVaw0GPloap6Ua-i_t2_6DKhEZ&ust=1669380945651000&source=images&cd=vfe&ved=0CBEQjhxqFwoTCMiInuPuxvsCFQAAAAAdAAAAABAe 2. .PANDUAN PENERAPAN DI UCLASSIFY : Ketikan Kalimat yang berhubungan dengan tawar-menawar prostitusi online 3. PENERAPAN UCLASSIFY DI PRODUK Di aplikasi Michat nantinya akan di terapkan Banned Chat ini untuk mengatasi pengembanngan Prostitisuti online dan menerapkan sistem deteksi user yang melakukan kalimat yang berbau prostitusi online di indonesia. Ini jika di terapkan dapat meningkatkan tatakrama dalam chatingan melalui Aplikasi MiChat.
by Modento
by luciazevedo
Makanan khas Batak
by Joseph_juntak
by tashaalyanst
by ErisGreyrat
by llaviosa
by MYoda
Movie Reviews: Recognizing Positive and Negative Text
by PheebsQ27
Data ini dibuat untuk mengetahui kategori kejahatan yang ada diwilayah medan
by wahyu07
Klasifikasi ini dibuat untuk mengetahui apa saja negara maju dan negara berkembang didunia.
by kristinamaretta
by insansimple
Created: 17 Nov 2022
by calvinnho
by hallka
by Nawel
by kimgav1946
by Rokua
textos em português e português de brasil.
by joao vieira
by Mojtaba1369