A New Term Frequency with Gaussian Technique for Text Classification and Sentiment Analysis

Abstract

This paper proposes a new term frequency with a Gaussian technique (TF-G) to classify the risk of suicide from Thai clinical notes and to perform sentiment analysis based on Thai customer reviews and English tweets of travelers that use US airline services. This research compared TF-G with term weighting techniques based on Thai text classification methods from previous researches, including the bag-of-words (BoW), term frequency (TF), term frequency-inverse document frequency (TF-IDF), and term frequency-inverse corpus document frequency (TF-ICF) techniques. Suicide risk classification and sentiment analysis were performed with the decision tree (DT), naïve Bayes (NB), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) techniques. The experimental results showed that TF-G is appropriate for feature extraction to classify the risk of suicide and to analyze the sentiments of customer reviews and tweets of travelers. The TF-G technique was more accurate than BoW, TF, TF-IDF and TF-ICF for term weighting in Thai suicide risk classification, for term weighting in sentiment analysis of Thai customer reviews for Burger King, Pizza Hut, and Sizzler restaurants, and for the sentiment analysis of English tweets of travelers using US airline services.

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“…Word embedding is a text classification technique that can be obtained using neural networks, where words are represented as real-valued vectors based on their context in natural language before using them through different models. Different word embedding algorithms are used to build vectors such as word2vec, GloVe [23] and term frequency [24].…”

“…Both Accuracy and F-score are calculated as shown in eqs. ( 21) to (24), where T p denotes the True Positives, and T n the True Negatives.…”