A sentiment analysis system for the Hindi language by integrating gated recurrent unit with genetic algorithm
المؤلفون المشاركون
Shrivastava, Kush
Kumar, Shishir
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 17، العدد 6 (30 نوفمبر/تشرين الثاني 2020)، ص ص. 954-964، 11ص.
الناشر
جامعة الزرقاء عمادة البحث العلمي
تاريخ النشر
2020-11-30
دولة النشر
الأردن
عدد الصفحات
11
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
The growing availability and popularity of opinion rich resources such as blogs, shopping websites, review portals, and social media platforms have attracted several researchers to perform the sentiment analysis task.
Unlike English, Chinese, Spanish, etc.
the availability of Indian languages such as Hindi, Telugu, Tamil, etc., over the web have also been increased at a rapid rate.
This research work understands the growing popularity of Hindi language in the web domain and considered it for the task of sentiment analysis.
The research work analyses the hidden sentiments from the movie reviews collected from the review section of Hindi language e-newspapers.
The reviews are multilingual, which makes sentiment analysis a challenging task.
To overcome the challenges, this research work proposes a deep learning based approach where a Gated Recurrent Unit network is combined with the Hindi word embedding model.
The strategy enables the network to efficiently capture the semantic and syntactic relation between Hindi words and accurately classify them into the sentiment classes.
Gated Recurrent Unit network's performance is profoundly dependent upon the selection of its hyper-parameters; therefore, this research work also utilizes a Genetic Algorithm to automatically build a gated recurrent network architecture enabling it to select the best optimal hyper-parameters.
It has been observed that the proposed Genetic Algorithm-Gated Recurrent Unit (GA-GRU) model is effective and achieves breakthrough performance results on the Hindi movie review dataset as compared to other traditional resource-based and machine learning approaches.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shrivastava, Kush& Kumar, Shishir. 2020. A sentiment analysis system for the Hindi language by integrating gated recurrent unit with genetic algorithm. The International Arab Journal of Information Technology،Vol. 17, no. 6, pp.954-964.
https://search.emarefa.net/detail/BIM-1434194
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shrivastava, Kush& Kumar, Shishir. A sentiment analysis system for the Hindi language by integrating gated recurrent unit with genetic algorithm. The International Arab Journal of Information Technology Vol. 17, no. 6 (Nov. 2020), pp.954-964.
https://search.emarefa.net/detail/BIM-1434194
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shrivastava, Kush& Kumar, Shishir. A sentiment analysis system for the Hindi language by integrating gated recurrent unit with genetic algorithm. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 6, pp.954-964.
https://search.emarefa.net/detail/BIM-1434194
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 962-964
رقم السجل
BIM-1434194
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر