Extraction of positive or negative opinions from any online content has received more consideration from researchers during the past decade, since the number of internet users that actively use online review sites, social networks and personal blogs to express their opinions has been growing. Sentiment analysis is the study of automated techniques for extracting sentiments from written languages making use of natural language processing tasks to thoroughly pre-process the data and extract polarity from the data. Customers who want to purchase products or services as well as business organizations, often rely on online reviews for knowing the overall user sentiment. Based on the sentiment, customers can choose whether or not to purchase a product while the organizations get an overall picture of their product. Aspect-based sentiment analysis helps in extraction of important features called aspects because knowing the polarity only is not sufficient. The proposed aspect-based sentiment analysis model uses polarity classification and sentiment extraction on reviews, and extracts the most interesting polarity aspects preferred by the customers automatically using both machine learning and deep learning algorithms. A search engine to pull out tweets and reviews relevant to user specified keyword is developed and corresponding interesting aspects are displayed.

中文翻译：

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基于方面的情感分析搜索引擎，用于社交媒体数据

在过去的十年中，由于积极使用在线评论网站，社交网络和个人博客表达意见的互联网用户数量不断增加，因此从任何在线内容中提取正面或负面观点都受到了研究人员的更多考虑。情感分析是对自动化技术的研究，该技术利用自然语言处理任务从书面语言中提取情感，以对数据进行彻底的预处理并从数据中提取极性。想要购买产品或服务的客户以及企业组织，通常依靠在线评论来了解整体用户情绪。根据客户的看法，客户可以选择在组织获得其产品总体情况时是否购买产品。基于方面的情感分析有助于提取称为方面的重要特征，因为仅了解极性是不够的。所提出的基于方面的情感分析模型使用极性分类和对评论的情感提取，并使用机器学习和深度学习算法自动提取客户最喜欢的最有趣的极性方面。开发了一种搜索引擎，用于提取与用户指定的关键字相关的推文和评论，并显示相应的有趣方面。并使用机器学习和深度学习算法自动提取出客户最喜欢的极性方面。开发了一种搜索引擎，用于提取与用户指定的关键字相关的推文和评论，并显示相应的有趣方面。并使用机器学习和深度学习算法自动提取出客户最喜欢的极性方面。开发了一种搜索引擎，用于提取与用户指定的关键字相关的推文和评论，并显示相应的有趣方面。