Current State of Text Sentiment Analysis from Opinion to Emotion Mining
Current State of Text Sentiment Analysis from Opinion to Emotion Mining
复制标题
DOI：
10.1145/3057270
复制
发表时间：
2017-06-01
影响因子：
16.6
通讯作者：
Zaiane, Osmar R.
中科院分区：
文献类型：
--
作者：
Yadollahi, Ali;Shahraki, Ameneh Gholipour;Zaiane, Osmar R.
从文本中进行情感分析包括提取关于作者对感兴趣的主题所传达的观点，情感甚至情感的信息。它通常等同于意见挖掘，但它也应该包括情感挖掘。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，但关注的是检测和分类作者对事件或主题的情感。文本情感挖掘方法有各种应用，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，根据用户情感推荐产品，甚至预测心理健康疾病。在关于情感分析的调查中，这些调查往往是旧的或不完整的，意见挖掘和情感挖掘之间的密切联系被低估了。这激发了对情感分析文献的不同和新的视角的需求，重点是情感挖掘。我们提出了最先进的方法，并提出以下贡献：（1）情感分析的分类;（2）极性分类方法和资源的调查，特别是那些与情感挖掘;（3）一个完整的调查情感理论和情感挖掘研究;和（4）一些有用的资源，包括词汇和数据集。
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.