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dc.contributor.authorChowdhury, Shaika
dc.contributor.authorChowdhury, Wasifa
dc.identifier.citationChowdhury, S., & Chowdhury, W. (2014). Performing sentiment analysis in bangla microblog posts. Paper presented at the 2014 International Conference on Informatics, Electronics and Vision, ICIEV 2014, doi:10.1109/ICIEV.2014.6850712en_US
dc.descriptionThis conference paper was presented in the International Conference on Informatics, Electronics and Vision, ICIEV 2014; Dhaka; Bangladesh; 23 May 2014 through 24 May 2014 [© 2014 IEEE] The conference paper's definite version is available at:
dc.description.abstractMuch of the research work on sentiment analysis has been carried out in the English language, but work in Bangla is limited to only news corpus and blogs. Microblogging sites are becoming a valuable source for publishing huge volumes of user-generated information, as users express their views, opinions, and sentiments over various topics. In this paper, we aim to automatically extract the sentiments or opinions conveyed by users from Bangla microblog posts and then identify the overall polarity of texts as either negative or positive. We use a semi-supervised bootstrapping approach for the development of the training corpus which avoids the need for labor intensive manual annotation. For classification, we use Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) and do a comparative analysis on the performance of these two machine learning algorithms by experimenting with a combination of various sets of features.en_US
dc.publisher© 2014 IEEE Computer Societyen_US
dc.subjectBangla microblog postsen_US
dc.subjectMaximum entropyen_US
dc.subjectSemi-supervised bootstrappingen_US
dc.subjectSentiment analysisen_US
dc.subjectSupport vector machineen_US
dc.titlePerforming sentiment analysis in Bangla microblog postsen_US
dc.typeConference paperen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University

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