Abstract
We present our supervised sentiment classification system which competed in SemEval2015 Task 10B: Sentiment Classification in Twitter— Message Polarity Classification. Our system employs a Support Vector Machine classifier trained using a number of features including n-grams, dependency parses, synset expansions, word prior polarities, and embedding clusters. Using weighted Support Vector Machines, to address the issue of class imbalance, our system obtains positive class F-scores of 0.701 and 0.656, and negative class F-scores of 0.515 and 0.478 over the training and test sets, respectively.
Highlights
Social media has seen unprecedented growth in recent years
We present a supervised learning approach, using Support Vector Machines (SVMs) for the task of automatic sentiment classification of Twitter posts
The F-scores were low for the negative class, which can be attributed to the class imbalance
Summary
Social media has seen unprecedented growth in recent years. For example, has over 645,750,000 users and grows by an estimated 135,000 users every day, generating 9,100 tweets per second). For example, has over 645,750,000 users and grows by an estimated 135,000 users every day, generating 9,100 tweets per second1) Users often express their views and emotions regarding a range of topics on social media platforms. While the benefits of using a resource such as Twitter include large volumes of data and direct access to enduser sentiments, there are several obstacles associated with the use of social media data.
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