استخراج دیتا ( نیاز به زبان NLP)

با توجه به متن ضمیمه شده که دیتای مدیا سنتیمنت رو از سایت بیگ دیتای GDELT برای بازار سهام چین استخراج کرده. میخوام عینا مثل همین متن دیتای شاخص مدیا سنتیمنت (Media sentiment) رو برای بیت کوین ( طبیعیتا کل کشور ها) از همین سایت GDELT project استخراج کنم در قالب اکسل. که نیاز به NLP داره از ChatGPT پرسیدم نحوه کار رو این توضیح رو بهم داد: Calculating the media sentiment index from GDELT with NLP is a complex task.! the best way to do this is to use a sentiment analysis tool such as Google Cloud Natural Language API or IBM Watson Natural Language Understanding. These tools can analyze text and return sentiment scores for each sentence or phrase. You can then use these scores to calculate an overall sentiment index for the GDELT data set. As an AI language model, I don't have access to your personal information or data. However, I can provide you with general steps to get media sentiment index from GDELT in Excel format: 1. Go to the GDELT website (https://www.gdeltproject.org/) and click on the "Explore" tab. 2. Select "GDELT Global Knowledge Graph" from the dropdown menu. 3. In the search bar, enter the keywords or topics you want to analyze. 4. Select the date range and other filters as per your requirement. 5. Click on "Search" and wait for the results to load. 6. Once the results are displayed, click on "Export" and select "Excel" as the file format. 7. Save the file on your computer. Note: The media sentiment index is calculated using natural language processing techniques that analyze news articles and other media sources for positive, negative, or neutral sentiment towards a particular topic or entity. GDELT provides several tools and APIs that can be used to extract this information in various formats. As an AI language model, I can suggest the following steps to calculate the media sentiment index from GDELT with NLP: 1. Collect the GDELT data: You can download the GDELT data from their official website or use their API to fetch the data. 2. Preprocess the data: Clean and preprocess the data by removing stop words, punctuations, and other irrelevant information. 3. Sentiment analysis: Use NLP techniques such as sentiment analysis to determine the sentiment of each article or news item in the dataset. 4. Calculate sentiment score: Assign a sentiment score to each article based on its sentiment analysis result. For example, you can assign a score of +1 for positive sentiments, -1 for negative sentiments, and 0 for neutral sentiments. 5. Aggregate scores: Aggregate all the scores for each day or week to get an overall sentiment score for that period. 6. Normalize scores: Normalize the scores by dividing them by the total number of articles in that period to get a media sentiment index between -1 and +1. 7. Visualize results: Visualize your results using graphs or charts to help you understand trends in media sentiment over time. I hope this help.