Structured Learning Based Turkish Sentiment Analysis

Oguz Ulgen, A. S. Ogrenci
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引用次数: 0

Abstract

Sentiment analysis is highly popular topic to identify people's opinions through the social media, forums and other websites. There are an abundance of opinions on internet and analysing those opinions would have many benefits for both private and public sectors. Research has evolved looking on tweets for mining opinions and for the classification of the tweets as positive, negative or neutral in its sentiment. In this research, Turkish tweets are used for sentiment extraction where a two layer neural network is used as the pattern recognition system. The supervised training of this system is based on structured learning. As a conclusion, structured learning seems to be helpful in pattern recognition to classify tweets and mining the opinions. However, it is evident that further research in data processing and training methodology is necessary to obtain reliable sentiment analysis results.
基于结构化学习的土耳其情感分析
情感分析是一个非常流行的话题,通过社交媒体、论坛和其他网站来识别人们的观点。互联网上有大量的观点,分析这些观点对私营部门和公共部门都有很多好处。研究已经发展到挖掘推文的观点,并将推文分类为积极、消极或中立的情绪。在本研究中,使用土耳其语推文进行情感提取,使用两层神经网络作为模式识别系统。该系统的监督训练是基于结构化学习的。综上所述,结构化学习似乎有助于对tweet进行分类和挖掘意见的模式识别。然而,为了获得可靠的情感分析结果,显然需要进一步研究数据处理和训练方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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