Approach:
1. Sentiment Analysis using Logistic Regression: For sentiment analysis of the stock market using logistic regression, we followed a structured approach: Data Collection : Textual data related to the stock market was collected from various articles published on financial news websites and journals. These articles provided valuable insights into market sentiment, capturing opinions, analyses, and discussions surrounding specific stocks or broader market trends. Data Preprocessing : The textual data extracted from articles underwent rigorous preprocessing to ensure its suitability for sentiment analysis. This involved cleaning the text by removing special characters, punctuation, and stopwords. Additionally, the text was tokenized and transformed into numerical features using techniques like TF-IDF (Term Frequency-Inverse Document Frequency). Model Training : The preprocessed data was split into training and testing sets. The logistic regression model was trained on the labeled training...
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