Question 1 Assignment title: Sentiment Analysis for Financial News Business case: A consulting firm has access to a large collection of financial news articles. They believe this treasure trove of textual

information has valuable insights. They are interested in understanding the sentiment often expressed in these articles towards various businesses and leverage that sentiment analysis to make informed financial decisions. However, manual analysis of these texts is time-consuming and may not always be accurate due to the complexities of human language. This is where your recent learning about NLP can be applied. Your task is to design an NLP model that can understand, analyse and classify the sentiment expressed in these financial news articles. Tasks: Here is a dataset (https://huggingface.co/datasets/financial phrasebank), which includes sentences from various financial news and their corresponding sentiment labels. The dataset is divided by agreement rate of 5-8 annotators. Please use 'Sentences_AllAgree.txt' which contains 2.26K rows of data for this project. (a) Design and implement relevant NLP workflow and techniques for analysing this dataset. Data preprocessing: Prepare and preprocess data to ensure it is in a suitable format (i)/nPlease use 'Sentences_AllAgree.txt' which contains 2.26K rows of data for this project. (a) Design and implement relevant NLP workflow and techniques for analysing this dataset. (i) Data preprocessing: Prepare and preprocess data to ensure it is in a suitable format for the relevant algorithms. (12 marks) (ii) Feature capturing: Demonstrate the statistics of the dataset to understand the data you have, not limited to building vocabulary, vocabulary visualization (for entire data and data with certain labels), etc. (15 marks)/n/n