Abstract
Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.
Highlights
Distribution of information supported by technological developments that better facilitate the public in obtaining information for free and in large numbers, one of which is textual information
Text mining is similar to data mining, a tool for data mining is designed for structured data from a database but text mining is designed for unstructured or semi-structured datasets such as word documents, emails, and more
After being given chi square treatment statistic and Term Frequency Inverse Document Frequency (TFIDF) support vector machine algorithm achieved the highest level of accuracy when the top value of K = 212 is 80.2% with an accuracy increase of 11.5%
Summary
Distribution of information supported by technological developments that better facilitate the public in obtaining information for free and in large numbers, one of which is textual information. Textual information can be categorized into two, namely the facts and opinions. Fact is an objective expression of an entity, event, or nature of an object. While opinion is a subjective expression that describes a person's sentiments, opinions, or feelings about an entity, event, and nature. Textual information can be processed using the text mining process. According to [2], text mining can be broadly defined as an intensive knowledge process where users interact with datasets using analytical tools. Text mining is known as text data mining [3]. Text mining is similar to data mining, a tool for data mining is designed for structured data from a database but text mining is designed for unstructured or semi-structured datasets such as word documents, emails, and more
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