Open milliondreamsblog opened 6 months ago
hello I'm Darshan, I would like to work on this issue. After reviewing the requirements, I believe I have both the interest and knowledge necessary to tackle it effectively. Please consider assigning this issue to me.
I request to be assigned this with appropriate GSSOC tags.
hello I'm Darshan, I would like to work on this issue. After reviewing the requirements, I believe I have both the interest and knowledge necessary to tackle it effectively. Please consider assigning this issue to me.
I request to be assigned this with appropriate GSSOC tags.
@milliondreamsblog is already working on it.You can work on other issues or create a new one.
pls assign me the issue,i have prior experience with the model task.
Sentiment Analysis:
Using transformer-based models like BERT or DistilBERT for sentiment analysis is a good choice, especially considering their effectiveness in capturing contextual information. I plan to fine-tune these models on Indian political news articles and Twitter posts, as these sources contain a wealth of information about public sentiment. Additionally, I'll utilize a 5-point rating scale (very good, good, moderate, bad, very bad) to provide granularity in sentiment analysis, which can be helpful for market analysis.
Financial Market Analysis:
Time series analysis techniques like ARIMA (AutoRegressive Integrated Moving Average) can be useful for analyzing financial market movements. ARIMA models are commonly used for forecasting future values based on past observations. If I'm not familiar with time series analysis yet, it's a good area to explore, especially for understanding and predicting market trends.
Correlation Analysis:
Once I have sentiment scores from the text data and market movements from the financial data, I can use statistical techniques like the Pearson correlation coefficient to identify any relationships between sentiment and market movements. The Pearson correlation coefficient measures the linear correlation between two variables, providing insights into how changes in sentiment relate to changes in financial market movements.
Overall, my approach integrates sentiment analysis with financial market analysis effectively. As I delve deeper into each component, gaining a solid understanding of time series analysis techniques like ARIMA will enhance my ability to uncover insights from financial data. Additionally, applying statistical methods like the Pearson correlation coefficient will help me quantify the relationship between sentiment and market movements accurately.