Lexicon-enhanced sentiment analysis framework using rule-based classification scheme

With the rapid increase in social networks and blogs, the social media services are increasingly being used by online communities to share their views and experiences about a particular product, policy and event. Due to economic importance of these reviews, there is growing trend of writing user reviews to promote a product. Nowadays, users prefer online blogs and review sites to purchase products. Therefore, user reviews are considered as an important source of information in Sentiment Analysis (SA) applications for decision making. In this work, we exploit the wealth of user reviews, available through the online forums, to analyze the semantic orientation of words by categorizing them into +ive and -ive classes to identify and classify emoticons, modifiers, general-purpose and domain-specific words expressed in the public’s feedback about the products. However, the un-supervised learning approach employed in previous studies is becoming less efficient due to data sparseness, low accur...随着社交网络与博客的蓬勃发展，社交媒体服务愈发被线上社群广泛用于分享针对特定产品、政策与事件的观点与使用体验。鉴于此类评论具备重要经济价值，通过撰写用户评论来推广产品的趋势日益凸显。当前，用户在选购产品时更倾向于参考线上博客与评论网站的内容。因此，用户评论成为情感分析（Sentiment Analysis, SA）应用中辅助决策的关键信息来源。本研究利用在线论坛中的海量用户评论资源，将词汇划分为积极类与消极类以分析其语义倾向性，借此识别并分类公众对产品的反馈中出现的表情符号、修饰词、通用词汇以及领域专属词汇。然而，过往研究所采用的无监督学习方法，因数据稀疏、准确率不足等问题，已逐渐难以维持理想的应用效果……