Papers by Jannatul Ferdoshi
Social media plays a vital role in our daily lives. To understand and interpret emotions and opin... more Social media plays a vital role in our daily lives. To understand and interpret emotions and opinions expressed on social media platforms, analyzing sentiment is very important. Our study is based on Twitter sentiment analysis. Our aim is to classify tweets automatically as positive, negative, or neutral based on their content using natural language processing and machine learning algorithms. The dataset we used for our analysis is extracted from the website called mendeley data and also we have added some tweets manually which covers various topics. To remove noise, including URLs, hashtags, punctuations, and user mentions, and to retain essential textual content and emojis, we pre-processed the dataset. Additionally, for our research, we used VADER (Valence Aware Dictionary and sentiment Reasoner) and Transformers-RoBERTa to analyze the sentiment of various tweets. We evaluate the performance of these two models using evaluation metrics such as accuracy, precision, recall and F1-score, and also confusion metrics on the testing set. We also discuss the study's limitations and conclude that machine learning-based sentiment analysis models are a reliable tool for the sentiment analysis of the twitter dataset.
2023 Computer Applications & Technological Solutions (CATS), 2024
Social media plays a vital role in our daily lives. To understand and interpret emotions and opin... more Social media plays a vital role in our daily lives. To understand and interpret emotions and opinions expressed on social media platforms, analyzing sentiment is very important. Our study is based on Twitter sentiment analysis. Our aim is to classify tweets automatically as positive, negative, or neutral based on their content using natural language processing and machine learning algorithms. The dataset we used for our analysis is extracted from the website called mendeley data and also we have added some tweets manually which covers various topics. To remove noise, including URLs, hashtags, punctuations, and user mentions, and to retain essential textual content and emojis, we pre-processed the dataset. Additionally, for our research, we used VADER (Valence Aware Dictionary and sentiment Reasoner) and Transformers-RoBERTa to analyze the sentiment of various tweets. We evaluate the performance of these two models using evaluation metrics such as accuracy, precision, recall and F1-score, and also confusion metrics on the testing set. We also discuss the study's limitations and conclude that machine learning-based sentiment analysis models are a reliable tool for the sentiment analysis of the twitter dataset.
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Papers by Jannatul Ferdoshi