[{"_1":2,"_181":-5,"_182":-5},"loaderData",{"_3":4,"_17":18,"_25":26,"_160":161},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","routes/_layout",{"_19":-5,"_20":21,"_22":21,"_23":11,"_24":21},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_88":89,"_111":112,"_149":-5,"_150":11,"_151":21,"_152":11,"_153":-5,"_154":11,"_155":-5,"_156":157,"_158":113,"_159":-7},"canonicalUrl","https://hub.docker.com/r/collabuniba/emtk/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","collabuniba","reponame","emtk","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":62,"_64":65,"_66":-5,"_67":68,"_69":70,"_71":11,"_72":33,"_73":74,"_79":11,"_80":81,"_85":33,"_86":87},"categories",[],"content_types",[44],"image","date_registered","2019-01-29T12:21:11.755325Z","description","Collab Emotion Mining Toolkit","is_private","last_updated","2020-04-30T18:32:44.582518Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",879,"repository_type","star_count",1,"status","status_description","active","source","collaborator_count",0,"full_description","# EMTK_docker\nDockerfile for building the EMTk image\n\n### Latest tag version\n`0.2.2` (2019-05-21)\n\n### Instructions\nFirst, execute the Docker container in interactive mode. By default, the instruction below will execute the `latest` version.\n\n```bash\n# docker run --rm -it collabuniba/emtk\n```\n\n\n##### Polarity module\n\nThe `-it` option starts the container in the interactive mode, so the `run` command logs you in the container’s shell environment (`>`). From there, to execute the polarity module, run:\n\n```bash\n> emtk polarity -F A -i input.csv -oc output.csv -vd 600 [-W dsm.bin] [-L] [-ul unigramList -bl bigramList]\n```\n\nwhere:\n\n- `-F {A, S, L, K}`, feature to evaluate `A` for All, S for Semantic, `L` for Lexicon, `K` for Keyword.\n- `-i `: the input data to classify. \n- `-oc `: the resulting predictions. \n- `-vd `: the vector size.\n- `-W `: [optional] the wordspace to use; the default wordspace is `/polarity/ClassificationTask/dsm.bin`.\n- `-L`: [optional] if present, the input corpus comes with a gold label in the label column.\n- `-ul `: [optional] the unigram's list.\n- `-bl `: [optional] the bigram's list.\n\nUsers can test-drive the polarity module by using the file `/polarity_sample.csv`, containing only a handful of documents.\n\n##### Emotion module\n\nRegarding the emotion classifier module, in the following, we show first how to train a new model and, then, how to test it on unseen data. To train a new model on a training set, run:\n\n```bash\n> emtk emotions train -i file.csv -d delimiter [-g] -e emotion\n```\n\nwhere:\n\n- `-i `: the corpus to be classified, encoded in UTF-8 without BOM and with the following format:\n\n- ```\n id;label;text\n …\n 22;NO;\"\"\"Excellent! This is exactly what I needed. Thanks!\"\"\"\n 23;YES;\"\"\"FEAR!!!!!!!!!!!\"\"\"\n …\n ```\n\n- `-d {c, sc}`: the delimiter used in the csv file, where c stands for comma and sc for semicolon.\n\n- `-e {joy, anger, sadness, love, surprise, fear}`: the emotion to be detected.\n\nAs a result, the script will generate an output folder in the present working directory named `training__/`, containing:\n\n- `n-grams/`: a subfolder containing the extracted n-grams.\n- `idfs/`: a subfolder containing the IDFs computed for n-grams and WordNet Affect emotion words.\n- `feature-.csv`: a file with the features extracted from the input corpus and used for training the model.\n- `liblinear/DownSampling/` and `liblinear/NoDownSampling/`, two folders each containing:\n - `trainingSet.csv` and `testSet.csv`.\n - eight models trained with `liblinear model__.Rda`, where ID refers to the liblinear model (with values in {0, ..., 7}).\n - `performance__.txt`, a file containing the results of the parameter tuning for the model (cost), the confusion matrix, and the Precision, Recall, and F-measure for the best cost for the specific ``.\n - `predictions__.csv`, containing the test instances with the predicted labels for the specific ``.\n\nFinally, to execute the classification task, run:\n\n```bash\n> emtk emotions classify -i file.csv -d delimiter -e emotion [-m model] [-f /path/to/.../idfs] [-o /path/to/.../ngrams] [-l]\n```\n\nwhere:\n\n- `-i `: same as above.\n- `-p`: enables the extraction of features regarding politeness, mood and modality.\n- `-d {c, sc}`: same as above.\n- `-e {joy, anger, sadness, love, surprise, fear}`: same as above.\n- `-m model`: [optional] the model file learned during the training step; if not specified, as default the model learned on the Stack Overflow gold standard will be used.\n- `-f /path/to/.../idfs`: [optional] with custom models, also the path to the folder containing the dictionaries with IDFs computed during the training step is required; the folder must include IDFs for n-grams (uni- and bi-grams) and the WordNet Affect lists of emotion words.\n- `-o /path/to/.../ngrams`: [optional] with custom models, also the path to the folder containing the dictionaries extracted during the training step; the folder must include n-grams (i.e., UnigramsList.txt and BigramsList.txt).\n- `-l`: [optional] if present, the input corpus comes with a gold label in the column label.\n\nAs a result, the script will create an output folder in the present working directory named `classification__`, containing:\n\n- `predictions_.csv`: a csv file, containing a binary prediction (yes/no) for each line of the input corpus:\n\n- ```\n id;predicted\n …\n 22;NO\n 23;YES\n …\n ```\n\n- `performance_.txt`: a file containing several performance metrics (Precision, Recall, F1, confusion matrix), created only if the input corpus `` contains the column label.\n\nUsers can test-drive the emotion classification module by using the file `/emotions_sample.csv`, which contains only a handful of documents. Other more complex sample datasets are available at `/emotions/java/DatasetSO/StackOverflowCSV`.\n\n##### The `/shared/` folder\n\nTo use the EMTk modules with **custom datasets**, users must access the `/shared/` folder, which is mounted specifying the `–v` option in the `docker run` command shown above. The `-v` option defines the paths for the folder to be shared in both the host and the hosted machines:\n\n`docker run -v : [...]`.\n\nFor instance, on a Linux machine, `-v ~/shared:/shared` creates a folder named `shared` in the host system's home (if it doesn't already exist) and a folder named `shared` in the container's root. Whatever is put into the shared folder can be found on both the systems, allowing input and output file exchange. This is accomplished leveraging [Docker's bind mounts](https://docs.docker.com/storage/bind-mounts/).\n\n","has_starred","hub_user","immutable_tags_settings",{"_75":11,"_76":77},"enabled","rules",[78],".*","is_automated","permissions",{"_82":21,"_83":11,"_84":11},"read","write","admin","user","storage_size",25498086269,"orgData",{"_90":91,"_92":93,"_94":95,"_96":33,"_97":98,"_99":100,"_101":102,"_103":104,"_105":106,"_107":98,"_108":98,"_109":98,"_110":21},"id","d20519553a0c42549cfb420372640a5d","uuid","d2051955-3a0c-4254-9cfb-420372640a5d","type","organization","orgname","full_name","","location","Bari, Italy","company","Uniba","profile_url","http://collab.di.uniba.it","date_joined","2015-10-12T15:44:31.725458Z","gravatar_url","gravatar_email","badge","is_active","recentTags",[113],{"_114":44,"_115":116,"_117":118,"_90":119,"_120":121,"_50":138,"_139":116,"_140":33,"_141":56,"_57":142,"_143":144,"_145":129,"_146":138,"_147":65,"_148":21},"content_type","creator",470328,"full_size",6576037396,56359941,"images",[122],{"_123":124,"_125":126,"_127":98,"_128":129,"_130":131,"_132":133,"_134":98,"_135":-5,"_136":118,"_63":65,"_137":-5},"architecture","amd64","digest","sha256:eb37d4ba81bf8c47fcdb1df76efb36c4f781ee94b2e9804681a3c8dc8c71e50c","features","last_pulled","2026-09-25T03:36:09.680622925Z","last_pushed","2020-04-30T18:32:44Z","os","linux","os_features","os_version","size","variant","2020-04-30T18:32:43.942649Z","last_updater","last_updater_username","media_type","latest","repository",6617087,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"selectedTag","attestationsUrl","routes/_layout.r.$namespace.$reponame.tags",{"_38":162,"_169":170,"_156":179,"_180":11},{"_40":163,"_42":164,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":165,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":62,"_64":65,"_66":-5,"_67":68,"_69":70,"_71":11,"_72":33,"_73":166,"_79":11,"_80":168,"_85":33,"_86":87},[],[44],[56],{"_75":11,"_76":167},[78],{"_82":21,"_83":11,"_84":11},"tags",{"_171":62,"_172":-5,"_173":-5,"_174":175},"count","next","previous","results",[176],{"_114":44,"_115":116,"_117":118,"_90":119,"_120":177,"_50":138,"_139":116,"_140":33,"_141":56,"_57":142,"_143":144,"_145":129,"_146":138,"_147":65,"_148":21},[178],{"_123":124,"_125":126,"_127":98,"_128":129,"_130":131,"_132":133,"_134":98,"_135":-5,"_136":118,"_63":65,"_137":-5},[],"canDeleteTags","actionData","errors"]

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