Browsing by Author "Das, Protiva"

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Sentiment Analysis on Twitter Tweets about Covid-19 Vaccines Using NlP and Supervised KNN Classification Algorithm
(Indonesian Journal of Electrical Engineering and Computer Science, 2021) Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Imran, M. M.; Muna, Jannatun Naeem; Billah, Md. Masum; Das, Protiva; Rahman, Md. Obaidur
The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Among them, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29%positive, 37.5% negative and 15.21% neutral, Moderna shows 46.16%positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08%positive, 40.06% negative and 13.86% neutral sentiment.
Sentiment analysis to determine employee job satisfaction using machine learning techniques
(BRAC University, 2023-01) Mouli, Nazifa; Das, Protiva; Bin Muquith, Munim; Biswas, Aurnab; Kabir Niloy, MD Dilshad; Karim, Dewan Ziaul; Ahmed, Md Faisal
Over the past three years, the COVID-19 epidemic had a significant impact on the labor market. Employees have been laid off and the majority of them have changed careers. If they can collect more datasets in the future, the researchers will be able to apply fine-tuning approaches to achieve perfect accuracy and precision. Incorporating hybrid models such as optimization techniques, multi-modal models, transfer learning models, hybrid deep learning models, sentiment models, etc. also broadens the scope of this study. These models can employ a variety of learning approaches, such as deep learning or traditional machine learning, and they can use many different types of data, such as text, images, or audio. The corpus was an additional strategy for improvement. These models consider lengthier texts in addition. 10% of US workers who keep their existing jobs are dissatisfied with them. Employee happiness is mostly influenced by business culture, but there are also cer tain economic and social elements that are interconnected. To ascertain the level of employee satisfaction and associated factors, significant study has been conducted. One of the most popular channels for opinion expression is social media. People now discuss the advantages and disadvantages of their work on the US-based social media site Glassdoor. For this study, total 1,56,428 data has been collected from Glassdoor.First, the data is correctly pre-processed after collection. The under standing of employee work satisfaction is provided by user ratings. For the purpose of making future predictions, the data was divided into binary class dataset and multiclass dataset. Moreover, this data is subjected to machine learning algorithms and deep learning algorithms. The best way to reach the ultimate conclusion is to use Bi-GRU for binary class dataset which has an overall accuracy of 97% and Bert model for multiclass dataset which has an accuracy of 95%.