{"slug":"gandalf1819-stock-market-sentiment-analysis","name":"Stock Market Sentiment Analysis","description":"Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka","long_description":"Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka\n\nImported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.","tagline":"Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka","category":"finance","tags":["finance","stocks","market-data","classification-algorithm","fundamental-analysis","machine-learning","naive-bayes-classifier","news-articles","r","random-forest"],"author":"gandalf1819","verified":false,"attribution":{"status":"community_indexed","statusLabel":"Community indexed","shortLabel":"COMMUNITY INDEXED","sourceLabel":"GitHub star discovery","sourceDetail":"gandalf1819/Stock-Market-Sentiment-Analysis","creatorName":"gandalf1819","creatorUrl":"https://github.com/gandalf1819","sourceUrl":"https://github.com/gandalf1819/Stock-Market-Sentiment-Analysis","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/gandalf1819-stock-market-sentiment-analysis#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"Stock Market Sentiment Analysis\" from https://github.com/gandalf1819/Stock-Market-Sentiment-Analysis into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/gandalf1819/Stock-Market-Sentiment-Analysis","github_repo":"gandalf1819/Stock-Market-Sentiment-Analysis","version":"1.0.0","license":"GPL-2.0","urls":{"web":"https://www.openagentskill.com/skills/gandalf1819-stock-market-sentiment-analysis","repository":"https://github.com/gandalf1819/Stock-Market-Sentiment-Analysis","api":"/api/agent/skills/gandalf1819-stock-market-sentiment-analysis","install_api":"/api/skills/gandalf1819-stock-market-sentiment-analysis/install"},"meta":{"created_at":"2026-06-21T06:58:06.858275+00:00","updated_at":"2026-06-21T06:58:06.858275+00:00","agent_friendly":true}}