# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 2 Related Work
(2018) established a framework (SAFOS) using emotional dictionaries with emotion polarity scores and thesaurus of Serbian WordNet (SWN) in the feature selection process in order to execute emotion analysis in Serbian. Numerous attempts have also been made to improve sentiment analysis techniques using deep learning. [PERSON] et al. (2018) propose a semantic enhanced convolutional neural network (SCNN) for sentiment analysis. Based on semiwordnet, a widely used emotional vocabulary resource, two methodsof word embedding and emotion embedding are input into a convolutional neural network classifier, and good experimental results are obtained. [PERSON] and [PERSON] (2016) proposed a long short-term memory language model (LSTM) for sentiment analysis. [PERSON] et al. (2017) propose a p-lstm model based on long-term memory recurrent neural network (LSTM). The experimental results show that p-lstm has good performance in emotion classification task. In order to cope with the limitations of existing pre-trained word vectors which are used as inputs for CNN, [PERSON] et al. (2018) propose a novel method, Improved Word Vectors (IWV). The IWV improves the accuracy of CNNs which are used for text classification tasks. [PERSON] and [PERSON] (2019) combines two different methods for sentiment analysis. The first is N-Stream ConvNets, which is a deep learning method, and the second is XGboost regression based on a set of embedded and dictionary-based features. [PERSON] et al. (2019) propose a method based on deep learning to classify the emotions expressed by users in comments (called RNSA). This method uses a unified feature set to analyze emotions, which represents word embedding, emotional knowledge, emotional transfer rules, statistics and speech knowledge. The experimental results show that the unified feature set learning method can achieve more significant performance than the feature set learning method. [PERSON] and [PERSON] (2018) further studies deep learning and microblog sentiment analysis, extracts data from microblog by crawler, preprocesses it by corpus, takes it as the input sample of the convolutional neural network, establishes a classifier based on SVM / RNN, and finally judges the sentiment orientation of each sentence in a given test set. The experimental results show that the scheme can effectively improve the accuracy of emotional orientation, and the verification results are ideal. [PERSON] et al. (2019) proposed a target-dependent convolutional neural network (TCNN) method of TLSA (target-level sentiment analysis) tasks. This method uses distance information on target words and neighboring words to understand their importance and achieve the classification task of extracting emotions from text targets. This approach is able to achieve better performance on single-target and multi-target datasets. [PERSON] et al. (2018) used BiLSTM and CNN neural network methods to improve the effect of sentiment analysis. In this approach, the BiLSTM-CRF sequence model is used to classify sentences into three types (no target, one target, multiple targets) based on the number of targets appearing in the sentence. Each set of sentences is then sent to a one-dimensional convolutional neural network of emotional classification. The experimental results show that the proposed method is able to improve the performance of sentence-level sentiment analysis and achieve the latest results from several benchmark datasets. [PERSON] et al. (2019) proposes an improved word vector (IWV) method for sentiment analysis. This method is based on part of speech tagging technology, word-based method, word location algorithm and word2 vec/glove method. The experimental results show that the improved word vector (IWV) is very effective for emotion analysis. [PERSON] et al. (2019) utilized a deep neural network based on convolutional expansion features to perform sentiment analysis on Chinese micro-blogs. The posts and comments on Chinese micro-blogs are integrated to form a micro-blog session.