—Facebook, Twitter, Weibo, and other social media and signiﬁcant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially the ﬁne-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for ﬁne-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma- chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Fine-Grained Sentiment Analysis: Recent Progress

Natural (Language Processing is a popular direction of research nowadays, and its sub field, sentiment analysis become one of the most active research area. With the development of sentiment analysis, lots of related softwares are emerging. For users' requirement, it is essential to figure out advantages and defaults of these softwares to improve their performance. Commercially available sentiment analysis software can be mainly divided into three categories. In this paper, we select three common and representative softwares in each of these three categories and test them through experiments on six real-word datasets. We design standardized steps and obtain experimental results from several angles. Through these results, we summary the characteristics and application scope of these softwares. They have problems of overfitting, semantic recognition error and defects in models. We point out the development direction of commercially available sentiment analysis software.

Application & Comparison of Several Sentiment Analysis Platforms with Open Source Review Dataset

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Application & Comparison of Several Sentiment Analysis Platforms with Open Source Review Dataset