Browsing by Author "Hasan, Mehadi"

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C Shopper
(Daffodil International University, 2021-06-12) Hasan, Mehadi
“C-Shopper” is a web based e-commerce site where you can find all kinds of chemical products of the ‘garments’ sector. Here all the ‘garments’ owners can easily buy the chemicals and raw materials they need in a very short time and at an affordable price. User can order by looking at the product details and the price of the product. When ordering the product, the user will fill and submit his information such as “username, address, Contact number, and mail id” in a completely accurate manner.
Predicting suicidal intent from social media text post using machine learning
(BRAC University, 2022-05) Chowdhury, Md. Mubin Ul Islam; Hasan, Mehadi; Nayem, A.K.M Muhibullah; Meem, Humaira Tasnim; Arif, Hossain
We are living in an age of modern science where cutting-edge technology has made the world so small. We can now easily connect with people worldwide via the internet and using social media. Social media has become a popular way to connect with people and to share our thoughts and feeling with the people we are connected. People are using social media as a tool where they share their feelings, daily life activities and so on. As a result, people are spending more times on those platforms to connect with people rather than in person. People who suffer from suicidal ideation are expressing their feelings and emotions on social platforms. As suicide is now an alarming problem in our society, we can use machine learning technology to determine suicidal ideation in the early stage based on social media data such as Twitter data and Reddit data. We have combined deep learning and an artificial neural network to make a model that we have named SIP (Suicidal Intent Prediction) which can detect suicidal ideation based on the text data of social media in the first place. In our proposed SIP model, we have used Functional, Word Embedding, Dense and GRU (Gated recurrent unit), Bi-directional LSTM, Bert to build our model. We have shown that our SIP model is able to determine the suicidal ideation with a higher training accuracy of 88%, a validation accuracy of 89% and training accuracy 98% and validation accuracy 99% from SIP (Sentiment) model.
Sentiment Analysis of comments having emoticons feedback
(Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh, 2018-11-15) Alam, Md. Mahfuz Ibn; Hasan, Mehadi
Sentiment analysis refers to the inference of people’s views, positions and attitudes in their written or spoken texts. Before the coining of the term, the field was studied under names such as subjectivity, point of view and opinion mining. Nowadays, the field is rapidly evolving due to the rise of new platforms such as blogs, social media and user-generated reviews. Two main research directions can be identified in the literature of sentiment analysis on microblogs. First direction is concerned with finding new methods to run such analysis, such as performing sentiment label propagation on Twitter follower graphs, and employing social relations for user-level sentiment analysis. The second direction is focused on identifying new sets of features to add to the trained model for sentiment identification, such as microblogging features including hashtags, emoticons, the presence of intensifiers such as all-caps and character repetitions etc., and sentiment topic features.