IJE TRANSACTIONS B: Applications Vol. 30, No. 11 (November 2017) 1707-1713   

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K. Srividya, K. Mariyababu and A. Mary Sowjanya
( Received: May 26, 2017 – Accepted in Revised Form: September 08, 2017 )

Abstract    As the internet and its applications are growing, E-commerce has become one of its rapid applications. Customers of E-commerce were provided with the opportunity to express their opinion about the product on the web as a text in the form of reviews. In the previous studies, mere founding sentiment from reviews was not helpful to get the exact opinion of the review. In this paper, we have used Aspect-Based Opinion Mining to get more interesting aspects of a product’s sentiment from unlabelled textual data. First, noun phrases algorithm was used to get all the aspect term of a review sentence. Secondly, the sentiment algorithm was applied on the result of the noun-phrase algorithm and also applied on adjectives and on adverbs. Finally, using relative importance algorithm important aspects were presented to the user. Our proposed methodology has achieved 77.03% of accuracy compared to previews studies. The proposed methodology can be applied for any product reviews in the form of text without any label, and it does not require any training dataset.


Keywords    Sentiment analysis, Opinion mining, Aspect term, Aspect based analysis, Customer review


چکیده    همانطور که اینترنت و برنامه های کاربردی آن در حال رشد هستند، تجارت الکترونیک یکی از کاربردهای سریع آن شده است. مشتریان تجارت الکترونیک با این فرصت برای ابراز نظرات خود در مورد محصول در وب به عنوان یک متن به شکل بررسی ایجاد شده اند. در مطالعات قبلی، احساسات پایه ای از بررسی ها به تنهایی برای نظر دقیق در مورد بررسی مفید نبودند. در این مقاله، از مفهوم نظرات مبتنی بر ابعاد استفاده کرده ایم تا جالب تر از احساسات محصول از داده های متنی بدون برچسب باشد. اولا، الگوریتم عبارات اسم برای بدست آوردن تمام جنبه های یک جمله بازبینی استفاده شد. دوم، الگوریتم احساسات در نتیجه الگوریتم عبارات اسم مورد استفاده قرار گرفت و همچنین بر روی صفت ها و قیدها اعمال شد. در نهایت، با استفاده از الگوریتم اهمیت نسبی، جنبه های مهم به کاربر ارائه شد. روش پیشنهادی ما نسبت به مطالعات پیشین به ۰۳/۷۷٪ دقت رسید. روش پیشنهادی می تواند برای بررسی هر محصول به صورت متن بدون برچسب استفاده شود و نیازی به مجموعه داده های آموزشی ندارد.


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