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Current State of Text Sentiment Analysis from Opinion to Emotion Mining

机译：从观点到情感挖掘的文本情感分析现状

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摘要

Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.
机译：文本中的情感分析包括提取有关观点，情感乃至作家针对感兴趣主题传达的情感的信息。它通常等同于观点挖掘，但也应包括情感挖掘。意见挖掘涉及使用自然语言处理和机器学习来确定作者对主题的态度。情感挖掘也使用类似的技术，但它涉及检测作家对事件或主题的情感并将其分类。文本情感挖掘方法具有多种应用，包括获取有关客户满意度的信息，帮助选择电子学习中的教材，基于用户情感推荐产品，甚至预测心理健康障碍。在对情感分析的调查中，这些调查通常是陈旧的或不完整的，人们低估了观点挖掘和情感挖掘之间的紧密联系。这激发了对情感分析文献的不同观点和新观点的需求，重点是情感挖掘。我们提出了最先进的方法，并提出了以下贡献：（1）情感分析的分类法； （2）关于极性分类方法和资源的调查，特别是与情感挖掘有关的方法和资源； （3）关于情感理论和情感挖掘研究的完整调查； （4）一些有用的资源，包括词典和数据集。

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