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Correlation of feature score to overall sentiment score for identifying the promising features

机译:特征分数与整体情感分数的相关性,用于识别有前途的特征

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Nowadays, most of the business intelligence focus on social media like facebook, twitter, blogs and online commercial websites like shopclues, pepperfry, flipkart, fabfurnish, testfreaks, amazon, greendust etc., to gather comments posted by the buyers in deciding about future demand,brand promotion, market segmentation and product penetration. In turn the buyers were also willing to post their comment about each of the products they buy through online. And these short reviews once refined and analyzed can help us to get a crystal clear opinion about the buyers' view which probably enhances the future buyers to make a buying decision based on spectacular features. This paper includes implementation on data acquisition, preprocessing, combinatory of lexicon and syntactic pattern mining approach (1) to find overall sentiment scores and correlate that score to that of feature score (2) to identify the most promising features of the product.
机译:如今,大多数商业智能都集中在社交媒体(如facebook,twitter,博客)和在线商业网站(如购物清单,胡椒,flipkart,织物,testfreaks,亚马逊,绿尘等)上,以收集购买者在决定未来需求时发表的评论。 ,品牌推广,市场细分和产品渗透率。反过来,购买者也愿意对他们通过在线购买的每种产品发表评论。这些简短的评论经过细化和分析后,可以帮助我们对买家的观点获得明确的意见,这可能会增强未来的买家根据出色功能做出购买决定的能力。本文包括数据采集,预处理,词典的组合以及句法模式挖掘方法的实现(1),以查找总体情感得分并将该得分与特征得分相关联(2),以识别产品最有前途的特征。

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