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<!doctype html>
<html lang="en" class="h-100">
<head>
<!-- Required meta tags -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="google-site-verification" content="UA-55422968-1">
<link rel="icon" href="https://jianliew.me/images/favicon.ico">
<!-- Bootstrap CSS -->
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet"
integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous">
<script async="" src="https://www.google-analytics.com/analytics.js"></script>
<script
type="application/javascript">var doNotTrack = !1; doNotTrack || (function (e, t, n, s, o, i, a) { e.GoogleAnalyticsObject = o, e[o] = e[o] || function () { (e[o].q = e[o].q || []).push(arguments) }, e[o].l = 1 * new Date, i = t.createElement(n), a = t.getElementsByTagName(n)[0], i.async = 1, i.src = s, a.parentNode.insertBefore(i, a) }(window, document, "script", "https://www.google-analytics.com/analytics.js", "ga"), ga("create", "UA-55422968-1", "auto"), ga("send", "pageview"))</script>
<title>Sentiment Analysis </title>
</head>
<body class="d-flex flex-column h-100">
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/js/bootstrap.bundle.min.js"
integrity="sha384-MrcW6ZMFYlzcLA8Nl+NtUVF0sA7MsXsP1UyJoMp4YLEuNSfAP+JcXn/tWtIaxVXM"
crossorigin="anonymous"></script>
<main class="flex-shrink-0">
<div class="container card card-body">
<h1>Sentiment Analysis</h1>
<p>Click <a href="https://jianliew.me/projects">here</a> to return to projects page</p>
<hr>
<p>
Sentiment analysis provides a way to understand the attitudes and opinions expressed in texts.
Each word can be assigned an emotion or sentiment such as positive or negative or other categories such as
happy,
joy, fear, etc. The sentiment of each word can be best identified for a particular problem. For example, when
studying positive and negative affects, one can ask people to identify whether a word shows positive or negative
meanings. This website provides several lexicons based on research to enable to see the result of a sentiment
analysis
based on several methodologies.
</p>
<p>
The repository for this project can be found <a href="http://github.com/JianLoong/sentimentanalysis">here</a>
</p>
<p>
<div class="alert alert-info" role="alert">
The lexicon mentioned in this page can be used freely for non-commercial research and educational purposes.
Please
contact
their original owners cited here for further questions.
</div>
</p>
<div class="card card-body bg-light mb-3 accordion" id="accordionExample">
<div class="accordion-item">
<h2 class="accordion-header" id="headingOne">
<button class="accordion-button collapsed" type="button" data-bs-toggle="collapse"
data-bs-target="#collapseOne" aria-expanded="true" aria-controls="collapseOne">
AFINN
</button>
</h2>
<div id="collapseOne" class="accordion-collapse collapse" aria-labelledby="headingOne"
data-bs-parent="#accordionExample">
<div class="accordion-body">
<p>
AFINN is a list of English words that are rated and give a value between -5 (negative) and +5
(positive). Afinn is the simplest yet popular lexicons used for sentiment analysis developed by Finn
Årup
Nielsen. It contains 3300+ words with a polarity score associated with each word. The implementation
here
uses Afinn-165
</p>
<p>
Scoring
<ul>
<li> > 0 - positive sentiment</li>
<li> 0 - neutral sentiment</li>
<li>
< 0 - negative sentiment </li>
</ul>
</p>
<p>
Finn Årup Nielsen, "A new ANEW: evaluation of a word list for sentiment analysis in microblogs",
Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big things come in small packages.
Volume 718 in CEUR Workshop Proceedings: 93-98. 2011 May. Matthew Rowe, Milan Stankovic, Aba-Sah Dadzie,
Mariann Hardey (editors)
</p>
<p>Please refer <a href="https://github.com/fnielsen/afinn">here</a> for more
information</p>
</div>
</div>
</div>
<div class="accordion-item">
<h2 class="accordion-header" id="headingFour">
<button class="accordion-button collapsed" type="button" data-bs-toggle="collapse"
data-bs-target="#collapseFour" aria-expanded="false" aria-controls="collapseFour">
VADER
</button>
</h2>
<div id="collapseFour" class="accordion-collapse collapse" aria-labelledby="headingThree"
data-bs-parent="#accordionExample">
<div class="accordion-body">
<p>
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis
tool that is specifically attuned to sentiments expressed in social media.
</p>
<p>
The Compound score is a metric that calculates the sum of all the lexicon ratings which have been
<ul>
<li>-1(most extreme negative) and +1 (most extreme positive).</li>
<li>positive sentiment : (compound score >= 0.05) </li>
<li>neutral sentiment : (compound score > -0.05) and (compound score < 0.05) </li>
<li>negative sentiment : (compound score <= -0.05) </li>
</ul>
</p>
<p>Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis
of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor,
MI, June 2014.
</p>
<p>Please refer <a href="https://github.com/cjhutto/vaderSentiment#introduction">here</a> for more
information</p>
</div>
</div>
</div>
<div class="accordion-item">
<h2 class="accordion-header" id="headingThree">
<button class="accordion-button collapsed" type="button" data-bs-toggle="collapse"
data-bs-target="#collapseThree" aria-expanded="false" aria-controls="collapseThree">
NRC Word-Emotion Association Lexicon
</button>
</h2>
<div id="collapseThree" class="accordion-collapse collapse" aria-labelledby="headingThree"
data-bs-parent="#accordionExample">
<div class="accordion-body">
<p>
The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions
(anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and
positive).
The annotations were manually done by crowd-sourcing.
</p>
<p>
Lexicon source is (C) 2016 National Research Council Canada (NRC) and this package is for research
purposes only. Source: http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm As per the terms of use
of
the NRC Emotion Lexicon, if you use the lexicon or any derivative from it, cite this paper:
Crowdsourcing
a Word-Emotion Association Lexicon, Saif Mohammad and Peter Turney, Computational Intelligence, 29 (3),
436-465, 2013.
</p>
<p>Please refer <a href="http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm">here</a> for more
information</p>
<p>Lexicon home page <a href="http://saifmohammad.com/WebPages/lexicons.html">here</a></p>
</div>
</div>
</div>
<div class="accordion-item">
<h2 class="accordion-header" id="headingSeven">
<button class="accordion-button collapsed" type="button" data-bs-toggle="collapse"
data-bs-target="#collapseSeven" aria-expanded="false" aria-controls="collapseSeven">
NRC Valence, Arousal, and Dominance (NRC-VAD) Lexicon
</button>
</h2>
<div id="collapseSeven" class="accordion-collapse collapse" aria-labelledby="headingSeven"
data-bs-parent="#accordionExample">
<div class="accordion-body">
<p>
The NRC Valence, Arousal, and Dominance (VAD) Lexicon includes a list of more than 20,000 English words
and their valence,
arousal, and dominance scores. For a given word and a dimension (V/A/D), the scores range from 0 (lowest
V/A/D) to 1 (highest V/A/D).
The lexicon with its fine-grained real-valued scores was created by manual annotation using Best--Worst
Scaling. The lexicon is markedly larger than any of the existing VAD lexicons
</p>
<p>
Words play a central role in language and thought. Several influential factor analysis studies have
shown
that the primary dimensions of word meaning are valence, arousal, and dominance (VAD) (Osgood et al.,
1957; Russell, 1980, 2003).
<ul>
<li>valence is the positive--negative or pleasure--displeasure dimension;</li>
<li>arousal is the excited--calm or active--passive dimension; and</li>
<li>dominance is the powerful--weak or 'have full control'--'have no control' dimension.</li>
</ul>
</p>
<p>Please refer <a href=https://saifmohammad.com/WebPages/nrc-vad.html">here</a> for more
information</p>
<p>Lexicon home page <a href="http://saifmohammad.com/WebPages/lexicons.html">here</a></p>
</div>
</div>
</div>
<div class="accordion-item">
<h2 class="accordion-header" id="headingFive">
<button class="accordion-button collapsed" type="button" data-bs-toggle="collapse"
data-bs-target="#collapseFive" aria-expanded="false" aria-controls="collapseFive">
Bing
</button>
</h2>
<div id="collapseFive" class="accordion-collapse collapse" aria-labelledby="headingFive"
data-bs-parent="#accordionExample">
<div class="accordion-body">
<p>
General purpose English sentiment lexicon that categorizes words in a binary fashion, either positive or
negative
</p>
<p>
Minqing Hu and Bing Liu, ``Mining and summarizing customer reviews.'', Proceedings of the ACM SIGKDD
International Conference on Knowledge Discovery & Data Mining (KDD-2004), Seattle, Washington, USA, Aug
22-25, 2004.
</p>
<p>Please refer <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html">here</a> for more
information</p>
</div>
</div>
</div>
<div class="accordion-item">
<h2 class="accordion-header" id="headingSix">
<button class="accordion-button collapsed" type="button" data-bs-toggle="collapse"
data-bs-target="#collapseSix" aria-expanded="false" aria-controls="collapseSix">
Loughran-Mcdonald
</button>
</h2>
<div id="collapseSix" class="accordion-collapse collapse" aria-labelledby="headingSix"
data-bs-parent="#accordionExample">
<div class="accordion-body">
<p>
English sentiment lexicon created for use with financial documents.
</p>
<p>
The sentiment categories are: negative, positive, uncertainty, litigious, strong modal, weak modal, and
constraining.
</p>
<p>
This sentiment dictionary, is modified to only categorize each word into its sentiment based on the
master
dictionary provided.
</p>
<p>Please refer <a href="https://sraf.nd.edu/loughranmcdonald-master-dictionary/">here</a> for more
information</p>
</div>
</div>
</div>
</div>
<div class="mb-3">
<label for="formGroupExampleInput2" class="form-label"><strong>Please select methodology:</strong></label>
<select id="selectedAlgorithm" class="form-select" aria-label="Default select example">
<option selected value="afinn-165">AFINN-165</option>
<option value="vader">VADER</option>
<option value="nrc">NRC Word-Emotion Association Lexicon</option>
<option value="nrc-vad">NRC Valence Arousal Dominance Lexicon</option>
<option value="bing">Bing</option>
<option value="loughran">Loughran-Mcdonald</option>
</select>
</div>
<div class="mb-3">
<label for="formGroupExampleInput2" class="form-label"><strong>Please select preprocessing
method:</strong></label>
<div class="form-check">
<input class="form-check-input" type="radio" name="flexRadioDefault" id="flexRadioDefault1" checked>
<label class="form-check-label" for="flexRadioDefault1">
None
</label>
</div>
<div class="form-check">
<input class="form-check-input" type="radio" name="flexRadioDefault" id="flexRadioDefault2" disabled>
<label class="form-check-label" for="flexRadioDefault2">
Stemming
</label>
</div>
<div class="form-check">
<input class="form-check-input" type="radio" name="flexRadioDefault" id="flexRadioDefault3" disabled>
<label class="form-check-label" for="flexRadioDefault3">
Lemmatization
</label>
</div>
</div>
<div class="mb-3">
<label for="exampleFormControlTextarea1" class="form-label"><strong>Enter some text below for real-time
sentiment
analysis:</strong></label>
<p><small>It is possible to copy and paste whole paragraphs</small></p>
<textarea class="form-control" rows="4" placeholder="" id="inputString">
</textarea>
</div>
<div class="mb-3">
<h2>Results</h2>
<hr>
<div class="verdict"></div>
<div class="result">Verdict will appear here</div>
</div>
<div class="mb-3">
<h2>Examples that could be used</h2>
<hr>
<ul>
<li>Good people sometimes have bad days</li>
<li>We are kind to good people</li>
<li>
I'd really truly love going out in this weather!
</li>
<li>
I do not dislike cabin cruisers
</li>
<li>
I love my mobile but would not recommend it to any of my colleagues.
</li>
</ul>
</div>
</div>
</main>
<p></p>
<footer class="footer mt-auto py-3 bg-light">
<div class="container">
<span class="text-muted">Project can be found <a
href="https://github.com/JianLoong/sentimentanalysis">here</a></span>
</div>
</footer>
<script src="./main.js"></script>
</body>
</html>