{"id":17303985,"url":"https://github.com/amirbar/reviews.sentiments.classifier","last_synced_at":"2026-01-06T17:04:59.775Z","repository":{"id":91170768,"uuid":"51999883","full_name":"amirbar/reviews.sentiments.classifier","owner":"amirbar","description":null,"archived":false,"fork":false,"pushed_at":"2016-02-21T14:41:30.000Z","size":12307,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-02-01T04:42:10.356Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Matlab","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amirbar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-02-18T10:33:10.000Z","updated_at":"2016-02-18T10:45:48.000Z","dependencies_parsed_at":"2023-05-05T22:27:16.167Z","dependency_job_id":null,"html_url":"https://github.com/amirbar/reviews.sentiments.classifier","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirbar%2Freviews.sentiments.classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirbar%2Freviews.sentiments.classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirbar%2Freviews.sentiments.classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirbar%2Freviews.sentiments.classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amirbar","download_url":"https://codeload.github.com/amirbar/reviews.sentiments.classifier/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245749905,"owners_count":20666086,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-15T11:51:51.056Z","updated_at":"2026-01-06T17:04:54.737Z","avatar_url":"https://github.com/amirbar.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Movies Reviews Sentiment Classifier\n\n#### This code aims to build a classifier on movie reviews. ####\nThe classification is binary, meaning we aim to predict whether a review is positive or negative.\n\n\n#### Step1: For each review in the dataset, convert the review to a fixed size vector. \n  1.1 Place the reviews in Data/neg \u0026 Data/pos.\n  1.2 Place the word2vec representation text file as: Data/vectors.txt.\n  1.3 Run InitialDatasetCreation/GenerateTrainingData.m.\n\n#### Step2: Train the model  \n  2.1 Run TrainSvmClassifier.m to output a model file.\n\n#### Step3: Classify new examples\n  3.1 Run go.m with input directory path param.\n  3.2 The script will create a new \"predicted.txt\" file in its folder.\n  3.3 Each line in the file specify \u003creview_filename\u003e \u003cprediction\u003e.\n  \n  \n#### Negative Review Example:\nThis was so lame that I turned the DVD off...maybe halfway through. It was so weak, I couldn't even pay full enough attention to tell you how far in I made it.Though I really wanted to believe that the depiction of the young Carlito would be somewhat different, I just couldn't buy it. I don't really blame the actors, because I think it was the script that may have fallen flat. I did find myself laughing a few times, but I don't think those lines were intended to be funny.\u003cbr /\u003e\u003cbr /\u003eIt's only saving grace is that I bought it in a 2 DVD set and I would have paid the price I did for the original alone. This is one of those cases when they should have let the classic stand alone.\n\n#### Positive Review Example:\nBrilliant over-acting by Lesley Ann Warren. Best dramatic hobo lady I have ever seen, and love scenes in clothes warehouse are second to none. The corn on face is a classic, as good as anything in Blazing Saddles. The take on lawyers is also superb. After being accused of being a turncoat, selling out his boss, and being dishonest the lawyer of Pepto Bolt shrugs indifferently \"I'm a lawyer\" he says. Three funny words. Jeffrey Tambor, a favorite from the later Larry Sanders show, is fantastic here too as a mad millionaire who wants to crush the ghetto. His character is more malevolent than usual. The hospital scene, and the scene where the homeless invade a demolition site, are all-time classics. Look for the legs scene and the two big diggers fighting (one bleeds). This movie gets better each time I see it (which is quite often).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirbar%2Freviews.sentiments.classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famirbar%2Freviews.sentiments.classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirbar%2Freviews.sentiments.classifier/lists"}