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{ "category": "PyCon US 2011", "language": "English", "slug": "pycon-2011--statistical-machine-learning-for-text", "speakers": [ "Olivier Grisel" ], "tags": [ "googlepredictionapi", "machinelearning", "nltk", "pycon", "pycon2011", "scikit-learn" ], "id": 417, "state": 1, "title": "Statistical machine learning for text classification with scikit-learn", "summary": "", "description": "Statistical machine learning for text classification with scikit-learn\n\nPresented by Olivier Grisel\n\nThe goal of this talk is to give a state-of-the-art overview of machine\nlearning algorithms applied to text classification tasks ranging from language\nand topic detection in tweets and web pages to sentiment analysis in consumer\nproducts reviews.\n\nAbstract\n\nUnstructured or semi-structured text data is ubiquitous thanks to the read-\nwrite nature of the web. However human authors are often lazy and don't fill-\nin structured metadata forms in web applications. It is however possible to\nautomate some structured knowledge extraction with simple and scalable\nstatistical learning tools implemented in python. For instance:\n\n * guessing the language and topic of tweets and web pages \n * analyze the sentiment (positive or negative) in consumer products reviews in blogs or customer emails \n\nThis talk will introduce the main operational steps of supervised learning:\n\n * extracting the relevant features from text documents \n * selecting the right machine learning algorithm to train a model for the task at hand \n * using the trained model on previously unseen documents \n * evaluating the predictive accuracy of the trained model \n\nWe will also demonstrate the results obtained for above tasks using the\n[scikit-learn]( package and compare it to\nother implementations such as [nltk]( and the [Google\nPrediction API](\n\n", "quality_notes": "", "copyright_text": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0", "embed": "", "thumbnail_url": "", "duration": null, "video_ogv_length": 168176911, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": "", "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": null, "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": null, "video_flv_download_only": false, "source_url": "", "whiteboard": "", "recorded": "2011-03-11", "added": "2012-02-23T04:20:00", "updated": "2014-04-08T20:28:28.035" }