Abstract
Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training-optimization process. In our scheme, the accuracy of sentiment classification is used as the optimization objective. The candidate sentiment lexicons are regarded as the individuals that need to be optimized. Then, two genetic algorithms are specially designed to adjust the values of sentiment words in lexicon. Finally, the best individual evolved in the presented genetic algorithms is selected as the sentiment lexicon. Our method only depends on some labelled texts and does not need any linguistic knowledge or prior knowledge. It provides a simple and easy way to construct a sentiment lexicon in a specific domain. Experiment results show that the proposed method has good flexibility and can generate high-quality sentiment lexicon in specific domains.
Highlights
It has become very convenient for people to express opinions and share knowledge through the Internet
E main innovation and contributions of this research are as follows: (i) A framework of constructing the sentiment lexicons for specific domains is proposed, in which the construction of sentiment lexicon is converted into a training and optimization process (ii) Our method extracts sentiment words from the short texts collected in the target domain, which breaks the limitation of seed lexicon and effectively improves the coverage of sentiment words of specific domains (iii) We specially design two genetic algorithms to optimize the sentiment lexicon, which makes it possible to automatically adjust the intensity values of sentiment words according to the domains
Inspired by the idea of machine learning, we propose a training-optimization framework to solve this problem, in which the construction of sentiment lexicons in a specific domain is transformed into a process of supervised learning. us, the presented framework provides a novel and effective way to construct sentiment lexicon and determine the value of sentiment word according to the sentiment orientations of texts
Summary
It has become very convenient for people to express opinions and share knowledge through the Internet. E main advantage of machine learning methods is that they do not depend on the linguistic knowledge and the domain knowledge and have good universality These machine learning methods can generate new sentiment words that are not related to the basic lexicon and break the limitation of basic lexicon. (i) A framework of constructing the sentiment lexicons for specific domains is proposed, in which the construction of sentiment lexicon is converted into a training and optimization process (ii) Our method extracts sentiment words from the short texts collected in the target domain, which breaks the limitation of seed lexicon and effectively improves the coverage of sentiment words of specific domains (iii) We specially design two genetic algorithms to optimize the sentiment lexicon, which makes it possible to automatically adjust the intensity values of sentiment words according to the domains.
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