Weka Text Classification for First Time & Beginner Users
Why Stack Overflow recommends it
Stack Overflow users link it when someone asks how to start with text classification or StringToWordVector in Weka, since it is a thorough GUI walkthrough that covers imbalanced data too.
Recommended by 5 people across 5 different months, 2014 to 2015.
What it is
A 59-minute beginner tutorial by Brandon Weinberg on text classification in the WEKA GUI. It walks through TextToDirectoryLoader, StringToWordVector settings, attribute selection, cost sensitivity and class imbalance, and a tour of classifiers, with timestamped sections.
What people said
Recommends Kalyanaraman Santhanam · 2014-01 · Stack Overflow · on “Weka: Text sentiment analysis on multiple text attributes”
I am looking to do text sentiment analysis on multiple text attributes. I followed this great beginners video tutorial [this video] which could be used for a single text attribute and its class - positive or negative. I want to extend the idea to multiple attributes simultaneously. To make clear, here's an example of what I am trying to do: Attributes collected from customers about a retail store: Store Experience review - String Collection review - String Assistance provided review - String Overall ranking - Integer (1 to 5) - class I want the analysis based on all the attributes (1 - 3) for the class attribute (4). If I tried using filter > unsupervised > attribute > StringToWordVector individu
Recommends rapid2share · 2014-02 · Stack Overflow · on “Java SVM Text Classification , Train & Test Files?”
yes, you should change the format to svm standard your svm classifier have no idea about text, first you should change your texts(train,test) to standrad format you can start your classifier with Weka [LINK] , weka have simple GUI & you can classify your datasets with few clicks when you get confidence about your classifier & it's accuracy then implement it in java you can use Weka in your java code too PS: 1- WEKA Text Classification for First Time & Beginner Users : [this video] 2- [LINK]
Recommends Atilla Ozgur · 2014-03 · Stack Overflow · on “Text Classification using Naive bayes”
You need to use weka StringToWord [LINK] filter and convert your text files to arff files. After that you can use weka classification algorithms. Watch following video [this video] to learn basics.
Recommends doxav · 2014-07 · Stack Overflow · on “Binary classification of dated documents with seasonal class variation”
It seems like it can be simplified into typical ML problems: text classification + imbalanced data + seasonality identification + architecture + typical batch/offline vs stream/online learning : Text classification: [this video] is a good tutorial on text classification with Weka and covers imbalance data issue. Seasonality identification: the goal is to enable the model to learn rules/inference on some different time attributes, so we should ease its job by extracting best known useful attributes. It means extracting typical date cycles (ie. week day, day of month, month, year...) and, if possible, also merge it with other more specific cycles or events (ie. elections, holidays, any custom cycle
Recommends cgl · 2015-04 · Stack Overflow · on “Weka ,Text Classification on an arff file”
I found the videos below quite helpful when I first got my hands on text classification using Weka. You might want to take a look. Weka Tutorial 31: Document Classification 1 (Application) [LINK] Weka Tutorial 32: Document classification 2 (Application) [LINK] WEKA Text Classification for First Time & Beginner Users [this video] You might want to use StringToWordVector filter to see the effect of each word as an attribute, which is indeed described in detail in the first and last video . Within the filter settings you can give a stopwords list and choose in each run to use it or not. Same with the stemming you can change it as well. This documentation [LINK] and videos will get you to understand
Cites user3560777 · 2014-05 · Stack Overflow · on “Which algorithm to use to classify data by using 3 different parameters?”
classification and clustering algorithms has implemented in many artificial intelligence software's such as MATLAB,WEKA,etc. you can see a sample of this in WEKA Text Classification for First Time & Beginner Users [this video] but i think your problem will have a good performance on MAP/REDUCE Freamework. I suggest you to use MAHOUT in your Problem which has a parallel framework and you can compromise your speed of other platforms with it.
Cites user_1177868 · 2014-07 · Stack Overflow · on “what is typical way to improve model precision/recall for text classification”
There are plenty of tools you can use in order to extract sensible linguistically-grounded feature types. It depends on what is your favourite programming language/environment and if you want to use a machine learning suite which has some text mining components in it, or a text mining component only. Have a look at: Java: Weka [LINK] ( video [this video] about text classification), OpenNLP [LINK] Python: Scikit-learn [LINK] and NLTK. About the stopword lists: [LINK] [LINK] [LINK] [LINK] [LINK]