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Tweet Analysis Based on Distinct Opinion of Social Media Users'

机译:基于社交媒体用户不同意见的推文分析

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The state of mind gets expressed via Emojis' and Text Messages for the huge population. Microblogging and social networking sites emerged as a popular communication channels among the internet users. Supervised text classifiers are used for sentimental analysis in both general and specific emotions detection with more accuracy. The main objective is to include intensity for predicting the different texts formats from twitter, by considering a text context associated with the emoticons and punctuations. The novel Future Prediction Architecture Based On Efficient Classification (FPAEC) is designed with various classification algorithms such as, Fisher's Linear Discriminant Classifier (FLDC), Support Vector Machine (SVM), Naïve Bayes Classifier (NBC) and Artificial Neural Network (ANN) Algorithm along with the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm. The priliminary stage is to analyze the distinct classification algorithm's efficiency, during the prediction process. Later, the classified data will be clustered to extract the required information from the trained data set using BIRCH method, for predicting the future. Finally, the perfomance of text analysis can get improved by using efficient classification algorithm.
机译:心态通过表情符号和短信传达给广大人口。微博和社交网站已成为互联网用户中流行的沟通渠道。受监督的文本分类器可在一般和特定的情绪检测中以更高的准确性进行情感分析。主要目标是通过考虑与表情符号和标点符号相关的文本上下文,来包括强度,以便从Twitter预测不同的文本格式。基于多种分类算法,例如Fisher线性判别分类器(FLDC),支持向量机(SVM),朴素贝叶斯分类器(NBC)和人工神经网络(ANN)算法,设计了基于有效分类的新型未来预测架构(FPAEC)以及BIRCH(使用层次结构的平衡迭代约简和聚类)聚类算法。首要阶段是在预测过程中分析独特分类算法的效率。之后,将使用BIRCH方法对分类数据进行聚类以从训练后的数据集中提取所需信息,以预测未来。最后,通过使用有效的分类算法可以提高文本分析的性能。

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