Text Sentiment Analysis Using Frequency-Based Vigorous Features

作     者：Abdul Razzaq Muhammad Asim Zulqrnain Ali Salman Qadri Imran Mumtaz Dost Muhammad Khan Qasim Niaz 

作者机构：Department of Computer ScienceMNS-University of AgricultureMultan60600Pakistan University of Management and Technology(UMT)Lahore54000Pakistan NFC Institute of Engineering&TechnologyMultan60600Pakistan Department of CS&ITThe Islamia University of Bahawalpur63100Pakistan Computer Science DepartmentUniversity of AgricultureFaisalabad38000Pakistan Department of Computer ScienceInstitute of Southern PunjabMultan60600Pakistan 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2019年第16卷第12期

页      面：145-153页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0839[工学-网络空间安全] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：China Scholarship Council  CSC 

主　　题：text mining sentiment analysis sentiment shifters KNN 

摘      要：Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.