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首页> 外文会议>Industry Sysposium on "Trends of Data Science and Applications: Theory and Practices";International Conference on Distributed Computing and Internet Technology >Evolution of Sentiment Analysis: Methodologies and Paradigms
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Evolution of Sentiment Analysis: Methodologies and Paradigms

机译：情绪分析的演变：方法和范式

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With the advent of the digital age, almost everything has come down to better understanding of the data. Natural language processing is equally in pursuit and is rather among the most researched areas of computer science. Post 1980, a major revolution in NLP embarked with the emergence of machine learning algorithms resulting from steady escalation in computational power. Unlike other data, text semantics becomes more complex both because of its contextual nature and daily evolving language usage. While the continuous efforts of improving language representation for logical units interpretation is still prevalent, much to our realization, traditional, and long established recurrent neural networks which were supposed to grasp a bi-directional context of language have been surpassed by attention models in constructing improved embeddings allowing systems to better understand language. Among numerous applications circumventing, understanding sentiment of text has been widespread in fields including but not limited to customer reviews, stock market, elections, healthcare analytics, online, and social media analytics. From binary classification of it to more challenging cases such as negation handling, sarcasm, toxicity, multiple attitudes, or polarity, this research chapter explores the evolution of sentiment analysis in the light of emerging text processing and the transition of text understanding from rule-based to a statistical one with a comparison of benchmark performance from state-of-the-art models over various applications and datasets.
机译：随着数字时代的出现，几乎一切都归结为更好地了解数据。自然语言处理同样追求，而且是计算机科学最多的研究领域。 1980年帖子，NLP的主要革命始于机器学习算法的出现，这是由计算能力稳定升级产生的机器学习算法。与其他数据不同，由于其上下文性质和日常不断发展的语言使用情况，文本语义变得更加复杂。虽然改善逻辑单位语言表现的不断努力仍然是普遍存在的，但对于应该掌握语言的双向语言的常规内容，仍然普遍存在，仍然是普遍存在的，很多都是由于构建改进的注意力而超越了语言的双向语言的双向背景嵌入式允许系统更好地了解语言。在众多的应用中，避难所以来，了解文本的情绪在包括但不限于客户评论，股票市场，选举，医疗分析，网上和社交媒体分析的领域，包括但不限于此。从二进制分类到更具挑战性的案件，如否定处理，讽刺，毒性，多重态度或极性，本研究章节鉴于新兴文本处理和从基于规则的文本理解的转换来探讨情绪分析的演变在各种应用程序和数据集中与最先进模型的基准性能比较统计一个统计文件。

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