Armenian Sentiment Analysis and Emotion Recognition Using Bidirectional Deep Learning Models

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

With the advancement of social applications, the number of people using such applications has increased to unbelievable levels. As of 2021, there are 2.8 billion users on Facebook. With such a large number of users, the amount of text data has also increased, which pushed data scientists' interest towards understanding such form of data. Text data may be used to extract information about sentiment and emotion which can be useful to many industries, such as businesses, election campaigns, entertainment, etc. As more and more people are joining the world wide web from all over the world, text data is being produced in many different languages, such as Russian, Chinese, Arabic, etc. For this reason, there has been a burst in the last few years in the development of natural language resources for the analysis of text in different languages. As Armenian is one of the ``new" languages on the Internet, very limited resources for analyzing Armenian exist out there. Hence, this thesis focuses on developing effective large-scale sentiment and emotion lexicons, which can be used to extract information from these data. Moreover, to further advance the resources available for Armenian NLP (Natural Language Processing), we develop an Armenian version of BERT (Bidirectional Encoder Representations from Transformers) by combining the approach used in developing the English BERT with a large corpus in Armenian.

روابط وملفات

الوثيقة في المستودع الخارجي عرض في AskZad

التعريف والنوع

رقم الوثيقة: 0e93b2fe-cc6f-4c4a-9c63-6521aeb74e17
رقم العقد: 0
نوع الوسائط: Crawler
نوع المحتوى: الرسائل العلمية
صيغة المصدر: رسائل ماجيستير
نوع الملف: pdf text
أسماء الملفات: 2206137_1.pdf

بيانات النشر

ألقاب المؤلفين: [{"name_ar":"Amer E. Mouawad ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Nigoghos Hovannes Kalayjian","title_ar":"اعداد","title_en":"Preparation"}]
اللغة: English

المصدر والدورية

اسم المصدر: Armenian Sentiment Analysis and Emotion Recognition Using Bidirectional Deep Learning Models

المحتوى والصفحات

عدد الصفحات: 0
كلمات الباحثين: Ensemble learning

إشراف وإعداد

الإشراف: Amer E. Mouawad
الإعداد: Nigoghos Hovannes Kalayjian

الاقتباسات الببليوغرافية

APA

Amer E. Mouawad و Nigoghos Hovannes Kalayjian. (2022). Armenian Sentiment Analysis and Emotion Recognition Using Bidirectional Deep Learning Models. أطروحة(رسائل ماجيستير). كلية العلوم والأداب-الجامعة الأمريكية في بيروت. لبنان.

MLA

Amer E. Mouawad و Nigoghos Hovannes Kalayjian. Armenian Sentiment Analysis and Emotion Recognition Using Bidirectional Deep Learning Models. 2022. كلية العلوم والأداب-الجامعة الأمريكية في بيروت، رسائل ماجيستير.