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CS5604 Fall 2016 Classification Team Final Report
Williamson, Eric R.
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Content is generated on the Web at an exponential rate. The type of content varies from text on a traditional webpage to text on social media portals (e.g., social network sites and microblogs). One such example of social media is the microblogging site Twitter. Twitter is known for its high level of activity during live events, natural disasters, and events of global importance. Improving text classification results on Twitter data would pave the way to categorize the tweets into human defined real world events. This would allow diverse stakeholder communities to interactively collect, organize, browse, visualize, analyze, summarize, and explore content and sources related to crises, disasters, human rights, inequality, population growth, resiliency, shootings, sustainability, violence, etc. Challenges with the data in the Twitter universe include that the text length is limited to 160 characters. Because of this limitation, the vocabulary in the Twitter universe has taken its own form of short abbreviations of sentences, emojis, hashtags, and other non-standard usage of written language. Consequently, traditional text classification techniques are not effective on tweets. Sophisticated text processing techniques like cleaning, lemmatizing, and removal of stop words and special characters will give us clean text which can be further processed to derive richer word semantic and syntactic relationships using state of the art feature selection techniques like Word2Vec. Machine learning techniques using word features that capture semantic and context relationships have been shown to give state of the art classification accuracy. To check the efficacy of our classifier, we would compare our experimental results with an association rules (AR) classifier. This classifier composes its rules around the most discriminating words in the training data. The hierarchy of rules along with an ability to tune to the support threshold makes it an effective classifier for scenarios where short text is involved. We developed a system where we read the tweets from HBase and write the classification label back after the classification step. We use domain oriented pre-processing on the tweets, and Word2Vec as the feature selection and transformation technique. We use a multi-class Logistic Regression algorithm for our classifier. We are able to achieve an F1 score of 0.96 when classifying a test set of 320 tweets across 9 classes. The AR classifier achieved an F1 score of 0.90 with the same data. Our developed system can classify collections of any size by utilizing a 20 node Hadoop cluster in a parallel fashion, through Spark. Our experiments suggest that the high accuracy score for our classifier can be primarily attributed to the pre-processing and feature selection techniques that we used. Understanding the Twitter universe vocabulary helped us frame the text cleaning and pre-processing rules used to eliminate noise from the text. The Word2Vec feature selection technique helps us capture the word contexts in a low dimensional feature space that results in high classification accuracy and low model training time. Utilizing the Spark framework to execute our classification pipeline in a distributed fashion allows us to classify large collections without running into out-of-memory exceptions.