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<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Dear Colleagues,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif;background:white">Please check our new published paper ML-NLPEmot: Machine Learning-Natural Language Processing Event-Based Emotion Detection Proactive Framework Addressing
Mental Health:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif"><br>
</span><a href="https://ieeexplore.ieee.org/document/10360126">https://ieeexplore.ieee.org/document/10360126</a><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif"><br>
<span style="background:white">An annotated dataset is available for researchers working on sentiments and emotion detection:<o:p></o:p></span></span></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif"><a href="https://github.com/INDUCE-Lab/ML-SocMedEmot">https://github.com/INDUCE-Lab/ML-SocMedEmot</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal">Please feel free to reach out with your invaluable comments and for research collaboration.<span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif"><br>
<br>
</span>“<o:p></o:p></p>
<p class="MsoNormal">Global rapidly evolving events, e.g., COVID-19, are usually followed by countermeasures and policies. As a reaction, the public tends to express their emotions on social media platforms. Therefore, predicting emotional responses to events
is critical to put a plan to avoid risky behaviors. This paper proposes a Machine Learning-Natural Language Processing-based framework to detect public emotions based on social media posts in response to specific events. It presents a precise measurement of
population-level emotions which can aid governance in monitoring public response and guide it to put in place strategies such as targeted monitoring of mental health, to react to a rise in negative emotions in response to lockdowns, or information campaigns,
for instance in response to elevated rates of fear in response to vaccination programs. We evaluate our framework by extracting 15,455 tweets. We annotate and categorize the emotions into 11 categories based on Plutchik’s study of emotion and extract the features
using a combination of Bag of Words and Term Frequency-Inverse Document Frequency. We filter 813 COVID-19 vaccine-related tweets and use them to demonstrate our framework’s effectiveness. Numerical evaluation of emotions prediction using Random Forest and
Logistic Regression shows that our framework predicts emotions with an accuracy up to 95.5%.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">“<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Best regards,<o:p></o:p></p>
<p class="MsoNormal">Leila<o:p></o:p></p>
<p class="MsoNormal" style="background:white"><span style="color:#212121"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D">---</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D">Leila Ismail, Ph.D.</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D">Associate Professor</span><span style="color:#201F1E">,
</span><span style="color:#1F497D">Dept. of Computer Science & Software Engineering</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><b><i><span style="color:#1F497D">Founding Director of Intelligent Distributed Computing & Systems (INDUCE) Laboratory<o:p></o:p></span></i></b></p>
<p class="MsoNormal" style="background:white"><i><span style="color:#201F1E"><o:p> </o:p></span></i></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D">College of Information Technology, United Arab Emirates University</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D;border:none windowtext 1.0pt;padding:0in">P.O.Box 15551, Al</span><span style="color:#1F497D">-Ain, United Arab Emirates</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D">Telephone: +971-3-7135530 (Office)<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:#1F497D">Email: </span>
<span lang="FR" style="color:#1F497D"><a href="mailto:leila@uaeu.ac.ae"><span lang="EN-US">leila@uaeu.ac.ae</span></a></span><span style="color:#1F497D"><o:p></o:p></span></p>
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<b style="font-family: Tahoma, Geneva, " sans-serif;?="">تنبيه: </b>"تنبيه: إن محتوى هذا البريد الإلكتروني بمرفقاته وبياناته وآرائه الواردة في هذه الوثيقة يحتوي على معلومات تعتبر ذات طبيعة سرية، وتستهدف المرسل اسمه فقط، فإذا لم تكن المرسل إليه في هذه الرسالة
أو كنت قد تلقيت الرسالة بالخطأ؛ يُرجى إبلاغ المُرسل وحذف الرسالة وأية ملفات مرتبطة من النظام الخاص بك، إذ ليس لديك الحق في نسخ أو طباعة أو توزيع أو استخدام محتويات هذا البريد الإلكتروني، أو السماح أو الكشف عن ذلك لأي طرف آخر تحت أي ظرف إلا بموافقة مسبقة من
لمرسل، علماً بأن إخلالك بما سبق سيعرضك للمساءلة القانونية" . </p>
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<b><span style="font-size:12px;font-family:Tahoma, Geneva, sans-serif;color:white; text-align:center;"><a href="http://www.uaeu.ac.ae/" style="color:#FFFFFF; text-decoration:none;">www.uaeu.ac.ae</a>
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