{"id":321,"date":"2025-12-20T18:30:25","date_gmt":"2025-12-20T18:30:25","guid":{"rendered":"https:\/\/rsdusane.info\/?page_id=321"},"modified":"2025-12-20T18:31:59","modified_gmt":"2025-12-20T18:31:59","slug":"financial-news-sentiment-analysis-system","status":"publish","type":"page","link":"https:\/\/rsdusane.info\/index.php\/financial-news-sentiment-analysis-system\/","title":{"rendered":"Financial News Sentiment Analysis System"},"content":{"rendered":"\t\t
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Problem:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Financial news contains large volumes of unstructured text that can influence market perception but is difficult to analyze at scale.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What I Built:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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I implemented an NLP pipeline that processes financial news articles, classifies sentiment, and aggregates insights to support market-aware analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Tech Stack:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Text Classification<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Data Analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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NLP<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Python<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Transformers<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Key Learnings:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tText preprocessing and representation\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tModel evaluation in NLP tasks\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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<\/div>Interpreting sentiment outputs\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Problem: Financial news contains large volumes of unstructured text that can influence market perception but is difficult to analyze at scale. What I Built: I implemented an NLP pipeline that processes financial news articles, classifies sentiment, and aggregates insights to support market-aware analysis. Tech Stack: Text Classification Data Analysis NLP Python Transformers Key Learnings: Text … <\/p>\n

Read more “Financial News Sentiment Analysis System”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"saved_in_kubio":false,"footnotes":""},"class_list":["post-321","page","type-page","status-publish","hentry"],"kubio_ai_page_context":{"short_desc":"","purpose":"general"},"featured_media_urls":[],"_links":{"self":[{"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/pages\/321","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/comments?post=321"}],"version-history":[{"count":7,"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/pages\/321\/revisions"}],"predecessor-version":[{"id":337,"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/pages\/321\/revisions\/337"}],"wp:attachment":[{"href":"https:\/\/rsdusane.info\/index.php\/wp-json\/wp\/v2\/media?parent=321"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}