<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning |</title><link>https://rajraghuwansh.github.io/tags/machine-learning/</link><atom:link href="https://rajraghuwansh.github.io/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><description>Machine Learning</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 01 Jun 2021 00:00:00 +0000</lastBuildDate><image><url>https://rajraghuwansh.github.io/media/icon_hu_fb558a5ed99f547e.png</url><title>Machine Learning</title><link>https://rajraghuwansh.github.io/tags/machine-learning/</link></image><item><title>Twitter Sentiment Analysis on Stocks</title><link>https://rajraghuwansh.github.io/projects/twitter-sentiments/</link><pubDate>Tue, 01 Jun 2021 00:00:00 +0000</pubDate><guid>https://rajraghuwansh.github.io/projects/twitter-sentiments/</guid><description>&lt;p&gt;This project analyzes &lt;strong&gt;Twitter sentiment related to stock market discussions&lt;/strong&gt; to understand how social media opinions correlate with stock movements.&lt;/p&gt;
&lt;p&gt;The goal of the project was to extract insights from large volumes of Twitter data and classify tweets based on their sentiment toward specific stocks. By applying &lt;strong&gt;Natural Language Processing (NLP)&lt;/strong&gt; and machine learning techniques, the model identifies whether tweets express positive, negative, or neutral sentiment.&lt;/p&gt;
&lt;p&gt;The workflow includes data preprocessing, feature extraction from text, sentiment classification, and analysis of sentiment trends related to financial markets. This project demonstrates how &lt;strong&gt;social media data can be leveraged for financial analytics and market sentiment understanding&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;You can explore the project repository here:&lt;/p&gt;
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