Abstract
Sentiment Analysis (SA) or opinion mining has recently become the focus of many researchers, because analysis of online text is beneficial and demanded for market research, scientific surveys from psychological and sociological perspective, political polls, business intelligence, enhancement of online shopping infrastructures, etc. This paper introduces a novel solution to SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare state-of-the-art SA methods against a novel hybrid method. The hybrid method utilizes a Sentiment Lexicon to generate a new set of features to train a linear Support Vector Machine (SVM) classifier. We further illustrate that our hybrid method outperforms the state-of-the-art unigram baseline.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
