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Register a new userCreating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi
إنشاء وتقييم الموارد لتحليل المعرفات في لغة الموارد المنخفضة: Sindhi
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في هذه الورقة، نطور Sindhi معجم شخصي باستخدام دمج الموارد الإنجليزية القائمة: NRC Lexicon، قائمة كلمات الرأي، Sentiwordnet، Sindhi-English Dictionary، وجمع معدلات Sindhi.يتم تعيين درجة المشاعر الإيجابية أو السلبية لكل كلمة sindhi رأي.بعد ذلك، نحدد تغطية المعجم المقترح مع تحليل الذاتية.علاوة على ذلك، نحن الزحف من سقسقة المجال سقسقة من الأخبار والرياضة والتمويل.يتم تفجيح Crescus Corpus من قبل Annetators ذوي الخبرة باستخدام أداة توضيح النص Doccano.يتم تقييم المشاعر المشروحة Corpus من خلال توظيف آلة ناقلات الدعم (SVM)، والشبكات العصبية المتكررة (RNN)، والشبكة العصبية التنافسية (CNN).
In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sentiment score is assigned to each Sindhi opinion word. Afterwards, we determine the coverage of the proposed lexicon with subjectivity analysis. Moreover, we crawl multi-domain tweet corpus of news, sports, and finance. The crawled corpus is annotated by experienced annotators using the Doccano text annotation tool. The sentiment annotated corpus is evaluated by employing support vector machine (SVM), recurrent neural network (RNN) variants, and convolutional neural network (CNN).
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