Abstract
With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.
Highlights
By integrating emoticons into the process of short-text sentiment analysis, it can more accurately judge the emotional tendency of short texts in social media such as Twitter and TikTok
The proposed model is superior to existing baseline models, including naïve Bayes and support vector machines for sentiment analysis [16]
The extensive use of emoticons such as emojis in short texts increases the amount of information covered in short-text content and boosts the entropy value of short-text information, which makes the prediction of short-text information content represented by sentiment characters difficult
Summary
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