Social media is not only used by the public to convey information but also to express emotions in writing. Text-based emotion detection is needed to find out the emotions contained in the text. This study combines CNN and BiLSTM for text-based emotion detection and compares the use of word embedding such as Word2Vec and GloVe. The data types used in this study are commuter line, transjakarta and commuter line + transjakarta. This study uses two scenarios, namely scenario I which classifies the dataset into emotion and no-emotion classes and scenario II which classifies emotions into five types of emotions, namely happiness, anger, sadness, fear, surprise. This study has two experimental types to emotion detection based on text, namely Word2Vec-CNN-BiLSTM, and GloVe-CNN-BiLSTM. In scenario I, Word2Vec-CNN-BiLSTM outperforms GloVe-CNN-BiLSTM in terms of accuracy. The accuracy values generated by Word2Vec-CNN-BiLSTM on the commuter line, transjakarta and commuter line + transjakarta data are 84.34%,83.%73%, and 83.88%, respectively. Word2Vec-CNN-BiLSTM provides the best overall Precision, Recall and F1-Measure compared with GloVe-CNN-BiLSTM on all data. The same result is also shown in scenario II where accuracy, precision, recall and F1-measure in Word2Vec-CNN-BiLSTM are better than other methods. The results of this study also improve the performance of emotion detection in text compared to the results of previous studies using Word2Vec-BiLSTM. 

Text-Based Emotion Detection using CNN-BiLSTM

Text pre-processing is an important component of a Chinese text classification. At present, however, most of the studies on this topic focus on exploring the influence of preprocessing methods on a few text classification algorithms using English text. In this paper we experimentally compared fifteen commonly used classifiers on two Chinese datasets using three widely used Chinese preprocessing methods that include word segmentation, Chinese specific stop word removal, and Chinese specific symbol removal. We then explored the influence of the preprocessing methods on the final classifications according to various conditions such as classification evaluation, combination style, and classifier selection. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. Our general conclusion is that the systematic use of preprocessing methods can have a positive impact on the classification of Chinese short text, using classification evaluation such as macro-F1, combination of preprocessing methods such as word segmentation, Chinese specific stop word and symbol removal, and classifier selection such as machine and deep learning models. We find that the best macro-f1s for categorizing text for the two datasets are 92.13% and 91.99%, which represent improvements of 0.3% and 2%, respectively over the compared baselines.

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification

Social media is not only used by the public to convey information but also to express emotions in writing. Text-based emotion detection is needed to find out the emotions contained in the text. This study combines CNN and BiLSTM for text-based emotion detection and compares the use of word embedding such as Word2Vec and GloVe. The data types used in this study are commuter line, transjakarta and commuter line + transjakarta. This study uses two scenarios, namely scenario I which classifies the dataset into emotion and no-emotion classes and scenario II which classifies emotions into five types of emotions, namely happiness, anger, sadness, fear, surprise. This study has two experimental types to emotion detection based on text, namely Word2Vec-CNN-BiLSTM, and GloVe-CNN-BiLSTM. In scenario I, Word2Vec-CNN-BiLSTM outperforms GloVe-CNN-BiLSTM in terms of accuracy. The accuracy values generated by Word2Vec-CNN-BiLSTM on the commuter line, transjakarta and commuter line + transjakarta data are 84.34%,83.%73%, and 83.88%, respectively. Word2Vec-CNN-BiLSTM provides the best overall Precision, Recall and F1-Measure compared with GloVe-CNN-BiLSTM on all data. The same result is also shown in scenario II where accuracy, precision, recall and F1-measure in Word2Vec-CNN-BiLSTM are better than other methods. The results of this study also improve the performance of emotion detection in text compared to the results of previous studies using Word2Vec-BiLSTM.

Chinese short text sentiment analysis can help understand society’s views on various hot topics. Many existing sentiment analysis methods are based on sentiment dictionaries. Still, sentiment dictionaries are easily affected by subjective factors. They require a lot of time to build as well as maintenance to prevent obsolescence. For the aim of extracting rich information within texts more effectively, we propose a Channel Attention TextCNN with Feature Word Extraction model (CAT-FWE). The feature word extraction module helps us choose words that affect the sentiment of reviews. Then, these words are integrated with multi-level semantic information to enhance the information of sentences. In addition, the channel attention textCNN module that is a promotion of traditional TextCNN tends to pay more attention to those meaningful features. It eliminates the impacts of features that do not make any sense effectively. We apply our CAT-FWE model to both fine-grained classification and binary classification tasks for Chinese short texts. Experiment results show that it can improve the performance of emotion recognition.

Channel Attention TextCNN with Feature Word Extraction for Chinese Sentiment Analysis

With the rapid development of mobile internet and the increase of social platforms, higher performance requirements are put forward for sentiment analysis of Chinese short texts. The traditional deep learning models based on CNN, LSTM and other frameworks face the problem of unable to extract all the effective information contained in the text because of the single direction of text parsing. However, complex model which combines framework in serial way exist a problem that cannot be ignored, that is it can not get effective training. To solve the above problems, this paper proposes an interpretable emotion analysis framework MIX-CNN-BiLSTM-Attention-Transformer (MCBAT Model), which can extract different features from multiple models. From the three dimensions of the fixed collocation of words, the context information and the importance of words in the text, CNN, BiLSTM-Attention and Transformer model are used to extract the above three different features. After vector splicing, the classification results are obtained by the classifier through the full connection layer. The accuracy and stability of the MCBAT model are improved compared with other classical emotion analysis models (CNN, BiLSTM, CNN-BiLSTM, etc.) and LSTM-LDA model. The model based on multi feature consideration is of great significance to emotion analysis task, and provides a method support for further development in the future.

Sentiment Analysis of Chinese Short Text Based on Multiple Features

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Sentiment Analysis of Chinese Short Text Based on Multiple Features