## 2 Related Work  
To the best of our knowledge, this study is a forerunner of spam RCS message filtering research, which is related to classification, opinion mining [4] and sentiment analysis [5] based on short text data. Several machine learning methods and models have been applied in this field, such as Naive Bayes [6], support vector machine (SVM) [7], vector space model (VSM) [8], decision tree [9] and k-nearest neighbor [10]. Recently, neural networks approaches [11], including deep learning models [12], have become popular in this field. All of these models are based on the Bag-of-words (BoW) assumption, which assumes words are independent from each other and ignores word order. For example, VSM and SVM models first represent features from a training text with proper weights in their feature extraction step, then rearrange these weights in the word-document matrix and perform subsequent learning algorithms in their classification step. These models make a great contribution to long text classification. However, difficulties and challenges emerge when applying these models to short text classification with the same feature extraction and classification steps. These difficulties originate from the information shortage of short texts and the sparsity of the word-document matrix [13]. As summarized in the review in [10], the four attributes of short texts hinder the effectiveness of these classical models. Their shortness results in the deficiency of text representation. Their diversity of topic and expression leads to the sparsity of the feature weight matrix. Their idiosyncratic language causes a great variety of synonyms and even obfuscated words. Furthermore, the instancy means that millions of short texts are produced daily and they require a high filtering speed. In order to overcome these difficulties, methods and models have been put forward to tackle these short text challenges. [PERSON] et al. [11] introduced lexical analysis, phrase structure and attention mechanisms for classifying the sentiment of Chinese short financial texts. Lexical analysis and phrase structure are overlooked in BoW assumption based models. To overcome the shortness issue, researchers rely on presenting a novel term weighting scheme [8; 10]. To tackle the feature-sparsity issue, [PERSON] et al. [14] proposed a non-negative matrix factorization feature expansion (NMFFE) approach by expanding short text features. [PERSON] et al. [15] enriched the number of features by generating term groups existing in the same context, and then proposed two basic supervised topic models for associating sentiments with topics. In addition, [PERSON] et al. [16] considered that word expansion methods might bring general concepts and noise into text representation and proposed a neural network called dual embeddings convolutional neural network (DE-CNN), which deploys an attention layer to extract and represent context-relevant concepts, then uses a convolutional neural network for short text classification. Other sparsity research includes online bitterm topic models (BTM) [17], bag of biterms modeling (BBM) [18], tax2 vec [19] and mixtures of Dirichlet-multinomial distributions [20]. To identify the obfuscated words in short text, [PERSON] et al. [21] put forward a generic framework for multilingual short text classification based on CNN. Researchers have also focused on character-level analysis to remove the side effects of informal words. [PERSON] et al. [22] proposed a mutual-attention CNN to concatenate word and character-level features by multiplying them into a matrix, and then stack the matrix as 3-dimensional tensors. To address the noisy and unstructured short text issues, [PERSON] et al. [23] presented a recommending system by proposing a sequence-to-sequence neural networks recommendation model, which represents user profiles and the conversational context. To increase short text classification speed, the supervised topic models presented by [PERSON] et al. [15] also include an accelerated algorithm.