A Feature Selection Method for Twitter News Classification

Abstract

Abstract-This paper presence a new feature selection method which can be used for creating data set in order to classify Twitter short messages. The Twitter short messages contain only 140 characters. Thus, the number of words per sentence is almost equal for all sentences. Once you pool the all text messages together, there can be number of words in the pool but, for a given sentence, there will be only few words included from the pool. This causes to have a sparse matrix as the feature vector. By removing the unrelated words from the feature space, the dimension can be reduced and therefore, the sparseness can be reduced. The unrelated words can be define as the common words (high frequent words) and noise words (low frequent words). Even though by removing these unrelated words, still it may contain some unrelated words. Thus, a feature selection technique was needed to apply in order to select the best feature set. The suggested new feature selection method was based on the Information Theory. It was named as Ratio Method. The calculated value increase when the word occurs frequently in a particular group and it decrease when the word occur in all groups. The best features can be choose by using a proper threshold. Some popular text classifiers such as SVM, Naïve Bayes and Decision Trees are used to evaluate the performance of the new feature selection method and to compare the new method with existing methods.

Review of Factors Affecting Efficiency of Twitter Data Sentiment Analysis

“…Chi-square method used for feature selection helps to reduce the dimensionality as well as noise in data and increase the accuracy of classifier from 81.5 to 92.3 in TF-IDF, 83 to 90 in FF and 83.1 to 93 in FP [21]. Number of feature selected highly effect the accuracy of result but after a limit increase in size of feature set does not increase classification accuracy [22]- [25].…”

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

“…Patil et al [11] in their research implemented SVM with and without feature extraction and show that SVM eliminated the need for feature selection due to the ability to generalize high dimension feature space. Inoshika et al [12] research on feature ranking and selection techniques for Twitter data opinion mining and suggested to remove unrelated words from feature space to reduce dimensionality that further reduces the sparseness of the feature set. The research also proposed a new feature selection technique on the basis of information theory named as Ratio Method.…”

Front-Page News Classification Model Based on the Stacking of Textual Context and Attribute Information

“…For example, the text classi cation models based on the support vector machine (SVM) classify news in each group of Twitter into positive and negative categories [18,19]. In further, more feature extraction methods are introduced to construct the text classification models based on SVM and its variants, which are applied for multitopic news classification [20,21]. In recent years, the quick development of deep neural networks promotes to study of these text classification models based on deep learning, which has become the mainstream in the field of text classification.…”