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Nowadays, SMS based communication is common in almost every sector. Private and government organizations deliver and receive millions of SMSs every year. This huge amount of SMSs can be used later in several useful ways. However, the storage space required for storing these huge amount SMSs creates so much complexity for the SMS service providing organizations that they cannot save the details of every SMS. Therefore, in this paper, we introduce a template-oriented SMS storage system based on a graph-theoretic approach, which reduces storage space requirements significantly. We achieve the storage space reduction through considering the fixed parts of SMSs as graph nodes and variable parts as graph edges. We perform experimentation on real SMS data collected from different telecom operators of Bangladesh. Our experimentation confirms that our proposed system substantially reduces the storage space requirement. To the best of our knowledge, we are the first to propose such a space-saving system for storing SMSs.

Social media has become important for sharing contents and people around the globe are expressing their feelings over social media. Analyzing these social interactions can be very much helpful to figure out the reasons behind people's feelings. In this work, we applied Long Short-term Memory (LSTM) Neural Networks for analyzing negative sentences in Bangla. We extracted negative Bangla tweets from twitter and also collected more negative Bangla sentences from people using google form. In our work, we have shown how hyper-parameters tuning can be useful for analyzing a small dataset and then applied to-fold cross-validation. Finally, we showed a relative comparison between our research and other previous works. This result will help psychologists to find out the reasons behind these social media interactions and prevent undesirable happenings resulted from negativity.

Human emotions like depression are inner sentiments of human beings which expose actual behaviors of a person. Analyzing and determining these type of emotions from people’s social activities in virtual world can be very helpful to understand their behaviors. Existing approaches may be useful for analyzing common sentiments, such as positive, negative or neutral expressions. However, human emotions, such as depression, are very critical and sometimes almost impossible to analyze using these approaches. In this work, we deployed Long Short Term Memory (LSTM) Deep Recurrent Network for depression analysis on Bangla social media data. We created a small dataset of Bangla tweets and stratified it. In this paper, we have shown the effects of hyper-parameter tuning and how it can be helpful for depression analysis on a small Bangla social media dataset. The result shows that 5 layered LSTM of size 128 with batch size 25, learning rate 0.0001 over 20 epochs, the depression detection accuracy is high for stratified dataset with repeated sampling. This result will help psychologists and other researchers to detect depression of individuals from their social activities in virtual world and help them to take necessary measures to prevent undesirable doings resulted from depression.

Nowadays, micro-blogging sites like Twitter, Facebook, YouTube, etc., have become much popular for social interactions. People are expressing their depression over social media, which can be analyzed to identify causes behind their depression. Most of the researches on emotion and depression analysis are based on questionnaires and academic interviews in non-Bengali languages, especially English. These traditional methods are not always suitable for detecting human depression. In this paper, we introduced Gated Recurrent Neural Network based depression analysis approach on Bangla social media data. We collected Bangla data from Twitter, Facebook and other sources. We selected four hyper-parameters, namely, number of Gated Recurrent Unit (GRU) layers, layer size, batch size and number of epochs, and presented step by step tuning for these Hyper-parameters. The results show the effects of these tuning steps and how the steps can be beneficial in configuring GRU models for gaining high accuracy on a significantly smaller data set. This will help psychologists and concerned authorities of society detect depression among Bangla speaking social media users. It will also help researchers to implement Natural Language Processing tasks with Deep Learning methods.