Smartphone applications play an increasingly significant part in our everyday lives, and their use has skyrocketed. The Google Play Store is a well-known plat-form through which one may obtain various Android applications whereas ap-plication like Ridesharing play a significant role in delivering public services more efficiently and effectively, as seen by the widespread adoption of many different types of innovative applications. This study focuses on users' reactions to these ridesharing applications, and it employs sentiment analysis to extract emotions from text reviews posted on the Google App Store platform given by the users. The primary goal is to examine the perspectives of customers and users of these applications. A total of 1818 data was gathered from the Google Play Store and divided into three categories: positive, negative, and neutral. The model was evaluated using the CNN, LSTM, and DistilBERT algorithms, with DistilBERT outperforming the others and achieving the highest accuracy of 98.84 %.

Sentiment analysis (SA) is a greater part of Natural Language Processing (NLP) in the research field. Previously a lot of work was done by SA in various languages. Most of the work was done in the English language. A little work was done in the Bengali language and its increasing day by day. Nowadays, Bangla News comments are very demanding for research work in the Bangla language. The procedures of text categorization, classifying, and different techniques for extracting features in textual information were discussed in this paper. Some Bangla newspaper dataset is currently available in online platforms. This research study has analyzed Bangla news comments sentiment using a hybrid approach and a pre-trained deep learning classifier. The proposed hybrid model utilizes an optimizer function “Adam” along with a word embedding “Glove”. The dataset utilized in the proposed model is collected from online platform Kaggle, the largest data science forum on the world. This dataset contains 13802 data. Using five different classes to our dataset. For building a model, this research study has applied preprocessing techniques, which makes a significant contribution in cleaning our dataset. This leads to get a well formed dataset. The proposed hybrid model has been combined with two familiar deep learning methods, namely BiLSTM and CNN. By comparing these two methods, the proposed hybrid model has gained better accuracy than the FastText model. The accuracy of hybrid model is about 89.89%.

In ODIs, World Cups and T-20 matches of various sports like Football, Cricket, Hockey, Basketball and Badminton, fans express their feelings and emotions towards the players by posting their status on social media like Facebook, Twitter etc. By collecting these opinions and feelings of the fans from different mediums, this research study has become more focused on a sentimental analysis of the sport with a total of 3759 comments related to Football (both national, international), Cricket, Hockey and Badminton. Since sports related opinions have been taken up in Bengali, global vector (glove) word embedding techniques are used for pre-processing which can retrieve word meanings and synthetic information. It also specializes in creating word vectors, including the structure of word embedding infrastructure, and provides a special advantage over statistics. Three models have been proposed in our study, one of which is a hybrid model of CNN-LSTM. In the proposed CNN-LSTM model, the CNN model is used to quote various features from word embedding that reflect short-term sentiment dependence while creating long-term sentimental relationships between LSTM words. In comparison to the hybrid model, two single models CNN and LSTM are proposed in five categories (i.e. Positive, Negative, Neutral, Happy, Sad). The sport's dataset integrates the CNN-LSTM hybrid model with the glove embedding layer, providing 97.45% accuracy. Lastly, the LSTM-CNN hybrid models perform comparatively better, realizing the feeling of the fans' comments.

Unwanted railroad level crossing accidents appear to be occurring on a daily basis at the moment. According to studies, these incidents occur due to the officers in charges or gatekeepers and pedestrians negligence during the level crossing. This haphazard manual system wastes time. Additionally, there is no prior system in place to prevent accidents if any vehicles or road users become stuck inside a railway crossing while the gate is closed. The primary goal of our work is to prevent undesirable accidents at railway level crossings and to reduce unnecessary time waste. This paper proposes a sensor-based system in which two sensors are used to detect the arrival and departure of the train, allowing the gate to open and close automatically. Another sensor detects any vehicles or humans stuck inside the gate and alerts the train operator via the GSM module. According to the information, the operator will take the necessary precautions to avoid any unwanted accidents.