Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and are not able to generate finer characterizations of sentiments as well as emotions involved. This paper describes a new opinion analysis scheme, i.e., a multi-level fine-scaled sentiment sensing with ambivalence handling. The ambivalence handler is presented in detail along with the strength-level tune parameters for analyzing the strength and the fine-scale of both positive or negative sentiments. It is capable of drilling deeper into text in order to reveal multi-level fine-scaled sentiments as well as different types of emotions.

中文翻译：

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具有矛盾处理的多级精细情感感知

社交媒体代表了丰富的信息来源，例如互联网用户在线发布的评论、反馈和其他意见。这些信息通常很好地反映了用户对各种服务、主题或产品的情绪和态度。情感分析已成为一项日益重要的自然语言处理 (NLP) 任务，以帮助用户了解互联网博客圈中正在发生的事情，它对公司和公共组织都很有用。然而，大多数现有的情绪分析技术只能在聚合级别分析数据，仅提供二元分类（正面与负面），并且无法生成更精细的情绪表征以及所涉及的情绪。本文描述了一种新的意见分析方案，即 具有矛盾处理的多级精细情感感知。矛盾处理程序与强度级别调整参数一起详细介绍，用于分析正面或负面情绪的强度和精细度。它能够更深入地挖掘文本，以揭示多层次的精细情绪以及不同类型的情绪。