Abstract
Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks.
Highlights
With the rapid increase in the popularity of social media applications, such as Twitter, a larger amount of sentiment data is being generated
In view of the specific emotion classification task, we propose the sentiment-aware word embedding based on the construction of a hybrid word vector method containing emotional information
We explain the use of the support vector machine (SVM), logistic regression model, decision tree model and gradient boost model classifier to evaluate the effectiveness of the emotional word vector
Summary
With the rapid increase in the popularity of social media applications, such as Twitter, a larger amount of sentiment data is being generated. Sentiment analysis for Chinese social network data has been gradually developed. Processing and Chinese Computing (NLPCC) established the task of evaluating the emotions of Weibo, which attracted many researchers and institutions. The conference drove the development of emotional analysis in China. Weibo sites have been the main communication tool. They provide information that is more up-to-date than conventional news sources, and this has encouraged researchers to analyze emotional information from this data source. There are many differences between Weibo text and traditional long text, such as movie reviews in sentiment analysis. They are short with no more than 140 characters. There are web-popular words, like “LanShouXiangGu” (a network language/buzzword, means feel awful and want to cry)
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