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index.html
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<!DOCTYPE html>
<html>
<head>
<link rel="icon" href="assets/favicon.png" type="image/png">
<title>Simple web Development Template</title>
<style>
html {
height: 100%;
}
.body {
background: lightgreen;
}
* {
margin: 0;
padding: 0;
color: white;
}
.navbar {
display: flex;
align-items: center;
justify-content: center;
position: sticky;
top: 0;
cursor: pointer;
}
.background {
background: black;
background-blend-mode: darken;
background-size: cover;
}
.nav-list {
width: 70%;
display: flex;
align-items: center;
}
.logo {
display: flex;
justify-content: center;
align-items: center;
}
.logo img {
width: 54px;
border-radius: 0px;
}
.nav-list li {
list-style: none;
padding: 26px 30px;
}
.nav-list li a {
text-decoration: none;
color: white;
}
.nav-list li a:hover {
color: grey;
}
.rightnav {
width: 30%;
text-align: right;
}
#search {
padding: 5px;
font-size: 17px;
border: 2px solid grey;
border-radius: 9px;
}
.Nothing{
background-image:linear-gradient(to bottom right, #33475b, #0033CC, #FF77CC, rgb(255, 122, 89));
}
.firstsection {
/* background-image:linear-gradient(to bottom right, #33475b, #0033CC, #FF77CC, rgb(255, 122, 89)); */
height: 400px;
}
.secondsection {
/* background-image:linear-gradient(to bottom right, #33475b, #0033CC, #FF77CC, rgb(255, 122, 89)); */
height: 400px;
}
.box-main {
display: flex;
justify-content: center;
align-items: center;
color: rgb(255, 255, 255);
max-width: 80%;
margin: auto;
height: 80%;
}
.firsthalf {
width: 100%;
display: flex;
flex-direction: column;
justify-content: center;
height: 19px;
margin: 82px 21px;
}
.secondhalf {
width: 30%;
}
.secondhalf img {
width: 70%;
border: 4px solid white;
border-radius: 150px;
display: block;
margin: auto;
}
.text-big {
font-family: 'Piazzolla', serif;
font-weight: bold;
font-size: 35px;
}
.text-small {
font-size: 18px;
}
.form-control{
color: black;
}
.btn {
padding: 8px 20px;
margin: 7px 0;
border: 2px solid white;
border-radius: 8px;
background: none;
color: white;
cursor: pointer;
}
.btn-sm {
padding: 6px 10px;
vertical-align: middle;
}
.section {
height: 400px;
display: flex;
align-items: center;
justify-content: center;
max-width: 90%;
margin: auto;
}
.section-Left {
flex-direction: row-reverse;
}
.paras {
padding: 0px 65px;
color: white;
}
.thumbnail img {
width: 250px;
border: 2px solid black;
border-radius: 26px;
margin-top: 19px;
}
.center {
text-align: center;
}
.card-header{
text-align: center;
margin: auto;
}
.mb-3{
align-items: center;
margin: 9px 628px;
margin: 16px 542px;
}
.text-footer {
text-align: center;
padding: 30px 0;
font-family: 'Ubuntu', sans-serif;
display: flex;
justify-content: center;
color: white;
}
html {
height: 100%;
}
.body {
background: linear-gradient(to bottom right, #33475b, #0033CC, #FF77CC, rgb(255, 122, 89));
}
</style>
</head>
<body>
<nav class="navbar background">
<ul class="nav-list">
<div class="logo">
<div class="cont s--inactive">
<a href="https://twitter.com/outliersapi" target="_blank" class="icon-link icon-link--twitter">
<img src="https://cdn1.iconfinder.com/data/icons/logotypes/32/twitter-128.png">
</a>
</div>
</div>
<li><a href="#web">About</a></li>
<li><a href="#program">Task description</a></li>
<li><a href="#course">Contact</a></li>
</ul>
</nav>
<section class="Nothing">
<section class="firstsection">
<div class="box-main">
<div class="firstHalf"><br><br><br><br>
<h1 class="text-big" id="web">Sentiment Analysis</h1>
<p class="text-small">
Sentiment analysis, also referred to as opinion mining,
is an approach to natural language processing (NLP) that
identifies the emotional tone behind a body of text.
This is a popular way for organizations to determine and
\categorize opinions about a product, service, or idea.
It involves the use of data mining, machine learning (ML) and
artificial intelligence (AI) to mine text for sentiment and
subjective information.Sentiment analysis systems help organizations
gather insights from unorganized and unstructured text that comes
from online sources such as emails, blog posts, support tickets,
web chats, social media channels, forums and comments. Algorithms
replace manual data processing by implementing rule-based, automatic
or hybrid methods. Rule-based systems perform sentiment analysis
based on predefined, lexicon-based rules while automatic systems
learn from data with machine learning techniques. A hybrid sentiment
analysis combines both approaches.
<br>
<br>
<br>
• To trail the growth of panic amongst Twitter® users based on a specific keyword<br>
• To analyze the sentiments of Indians post lockdown imposed by the government<br>
• To extract an exact idea by detecting the primary topics tweeted by netizens related to COVID-19 pandemic.<br>
• To study how the Chinese Weibo users were affected emotionally on and after 20th January, 2020.
</p>
</div>
</div>
</section>
<section class="secondsection">
<div class="container">
<div class="row">
<div class="col-md-12">
<div class="card mt-4">
<div class="card-header">
<h4>Search Tweets Here</h4>
</div>
<div class="card-body">
<div class="row">
<div class="col-md-7">
<form action="" method="GET">
<div class="input-group mb-3">
<input type="text" name="search"class="form-control" placeholder="Write Tweets">
<button type="submit" class="btn btn-primary">Search</button>
</div>
</form>
</div>
</div>
</div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-md-12">
<div class="card mt-4">
</div>
<div class="card-body">
<div class="row">
<div class="col-md-7">
<form action="/" method="GET">
<div class="input-group mb-3">
<input type="text" name="search" class="form-control" placeholder="Sentiment">
</div>
</form>
</div>
</div>
</div>
</div>
</div>
<div class="box-main">
<div class="firstHalf">
<h1 class="text-big" id="program">
<br>
Task description
</h1>
<p class="text-small">
This competition is for sentiment analysis of Tweets related to the Covid-19 pandemic, which is
a multi-label text classification task. Since the outbreak of coronavirus, it has affected more
than 180 countries where massive losses in the economy and jobs globally and confining about 58%
of the global population are caused. The research on people’s feelings is essential for keeping
mental health and informed about Covid-19. In this competition, the released training data contains
1.6 million labeled tweets while the released validation data have 10,000 pieces of unlabeled
tweets. The training data have 6 columns, containing Target, Tweet ID, Date, Flag, User and Text.
Note that the orders are shown as , Negative(0), Neutral(2), Positive (4). For example, if the labels
is 0, it means that this piece of the tweet is labeled as Neutral . The public ranking of the competition
will be based on the prediction on the validation dataset. The final ranking will be based on a private
testing dataset that has the same distribution as the training and validation dataset.
<br>
<br>
• Python Code-> Twitter API->Login to twitter account using generated key.<br>
• Topic selection ->Time selection->Extraction of related tweets in a CSV file(testing dataset).<br>
• Data cleansing of testing dataset->Cleaned dataset(testing).<br>
• Neural network model training using training dataset.<br>
• Generating the label of sentiments of the extracted tweets using the model.<br>
• Checking of classification accuracy
</p>
</div>
</div>
</section>
<section class="section">
<div class="paras">
<br>
<br>
<br>
<br>
<br>
<br>
<br>
<br>
<br>
<h1 class="sectionTag text-big">Future Scope</h1>
<p class="sectionSubTag text-small">
We can further improve our progress by making it real-time... i.e when someone
will tweet something, it will go through our model and will automatically get the
label context immediately on the Twitter app itself.
<br>
<br>
<br>
<br>
<center>Team Outliers<br>
<center>E-mail - [email protected]
</p>
</div>
</section>
</section>
<footer class="background">
<p class="text-footer">
Copyright ©-All rights are reserved
</p>
</footer>
</body>
</html>