Scopus Indexed Paper

Paper Details


Title
Sentiment Extraction From Bangla Text : A Character Level Supervised Recurrent Neural Network Approach
Abstract
Over the recent years, people are heavily getting involved in the virtual world to express their opinions and feelings. Each second, hundreds of thousands of data are being gathered in the social media sites. Extraction of information from these data and finding their sentiments is known as a sentiment analysis. Sentiment analysis (SA) is an autonomous text summarization and analysis system. It is one of the most active research areas in the field of NLP and also widely studied in data mining, web mining and text mining. The significance of sentiment analysis is picking up day by day due to its direct impact on various businesses. However, it is not so straightforward to extract the sentiments when it comes to the Bangla language because of its complex grammatical structure. In this paper, a deep learning model was developed to train with Bangla language and mine the underlying sentiments. A critical analysis was performed to compare with a different deep learning model across different representation of words. The main idea is to represent Bangla sentence based on characters and extract information from the characters using a Recurrent Neural Network (RNN). These extracted information are decoded as positive, negative and neutral sentiment.
Keywords
Sentiment analysis, Training, Mathematical model, Logic gates, Recurrent neural networks, Data mining, Facebook
Authors
Mohammad Salman Haydar ; Mustakim Al Helal ; Syed Akhter Hossain
Phone
Journal or Conference Name
International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018
Publish Year
2018
Indexing
Scopus