Abstract

This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system.

Highlights

  • IntroductionWe have demands to process and mine from Social networks and online platforms

  • This paper describes a participation in SemEval-2017 Task 4 with the ej-sa-2017 system

  • We have participated in SemEval-2017 Task 4 on Sentiment Analysis in Twitter, subtasks A (Message Polarity Classification), B (TopicBased Message Polarity Classification)(Rosenthal et al, 2017)

Read more

Summary

Introduction

We have demands to process and mine from Social networks and online platforms. Opinions in usergenerated content, are valuable for market and trend analysis. Processing of sentiment analysis helps us to automatically distinguish from these written opinions. This paper describes a participation in SemEval-2017 Task 4 with the ej-sa-2017 system. We have participated in SemEval-2017 Task 4 on Sentiment Analysis in Twitter, subtasks A (Message Polarity Classification), B (TopicBased Message Polarity Classification)(Rosenthal et al, 2017). Subtask A is to classify message polarity from given a message that is of positive, negative, or neutral sentiment. Subtask B is to classify positive or negative sentiment of a tweet towards that topic on a two-point scale

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.