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Improving sentiment analysis efficacy through feature synchronization
Multimedia Tools and Applications ( IF 3.6 ) Pub Date : 2021-01-14 , DOI: 10.1007/s11042-020-10383-w
Zulqurnain Ali , Abdul Razzaq , Sajid Ali , Sulman Qadri , Azam Zia

Social media platforms are becoming a rich source of valuable information through sharing and publishing user generated reviews and comments. The identification and extraction of subjective information from a piece of text is a crucial challenge in sentiment analysis. Numerous techniques have been proposed that aimed to analyze the sentiments of the text. However, accuracy was compromised due to inadequate identification of intensity, sentiments shifter and negation of words as well as divergence between tweets and their associated labels. In this study, Prescriptive Sentiment Analysis (PSA) based on features synchronization has been introduced for increasing accuracy in text sentiment analysis. At first, pre-processing has been performed which includes removal of stop words and tokenizing the text into sentiment words, intensity words, sentiment shifters and negation words. Secondly; polarity of intensity words, clauses and sentiment shifters in the text are calculated. Identification and removal of ambiguity between extracted features and their associated labels have been accomplished through feature synchronization. The K-Nearest Neighbor (KNN) has been implemented to predict text trend based on synchronized features. The proposed approach has been evaluated on publicly available datasets of twitter and movie reviews. Experimental results show significant improvement in sentiment analysis efficiency as compared to other baseline methods.



中文翻译:

通过特征同步提高情感分析功效

通过共享和发布用户生成的评论和评论,社交媒体平台正在成为有价值信息的丰富来源。从一段文本中识别和提取主观信息是情感分析中的关键挑战。已经提出了许多旨在分析文本情感的技术。但是,由于强度识别不充分,情感转移和单词否定以及鸣叫与其相关标签之间的差异,准确性受到了影响。在这项研究中,基于特征同步的说明性情感分析(PSA)已被引入,以提高文本情感分析的准确性。首先,已进行了预处理,包括删除停用词并将文本标记为情感词,强度词,情绪转移者和否定词。其次; 计算文本中强度词,从句和情感转移词的极性。识别出的特征及其相关标签之间的歧义可以通过特征同步来实现。已实现K最近邻居(KNN)以基于同步功能预测文本趋势。该提议的方法已经在Twitter和电影评论的公开可用数据集上进行了评估。实验结果表明,与其他基线方法相比,情感分析效率得到了显着提高。识别出的特征及其相关标签之间的歧义可以通过特征同步来实现。已实现K最近邻居(KNN)以基于同步功能预测文本趋势。该提议的方法已经在Twitter和电影评论的公开可用数据集上进行了评估。实验结果表明,与其他基准方法相比,情感分析效率得到了显着提高。识别出的特征及其相关标签之间的歧义可以通过特征同步来实现。已实现K最近邻居(KNN)以基于同步功能预测文本趋势。该提议的方法已经在Twitter和电影评论的公开可用数据集上进行了评估。实验结果表明,与其他基线方法相比,情感分析效率得到了显着提高。

更新日期:2021-01-14
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