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

Read more

Summary

Introduction

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.

Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.