#EMOJI FOR TWITTER DOWNLOAD SERIES#
Additionally, we conduct a series of ablation and comparison experiments to investigate the effectiveness of our model.Įmojis have been widely used in social media as a new way to express various emotions and personalities.
Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets. Finally, we use the transfer learning method to increase converge speed and achieve higher accuracy. Our model designs a co-attention mechanism to incorporate the text and emojis, and integrates a squeeze-and-excitation (SE) block into a convolutional neural network as a classifier. In EmoGraph2vec, we form an emoji co-occurrence network on real social data and enrich the semantic information based on an external knowledge base EmojiNet to obtain emoji node embeddings. In this work, we propose a method to learn emoji representations called EmoGraph2vec and design an emoji-aware co-attention network that learns the mutual emotional semantics between text and emojis on short texts of social media. It results that the emotional semantics of emojis cannot be fully explored. As for the sentiment analysis task, many researchers ignore the emotional impact of the interaction between text and emojis. However, when it comes to emoji representation learning, most studies have only utilized the fixed descriptions provided by the Unicode Consortium, without consideration of actual usage scenario. Many emojis are used to strengthen the emotional expressions and the emojis that co-occurs in a sentence also have a strong sentiment connection. In social media platforms, emojis have an extremely high occurrence in computer-mediated communications. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.
The sentence matrix is input into a convolution neural network classification model for classification. 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. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. In addition, short texts contain emojis to make the communication immersive. 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. With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. This article can be used to develop new approaches, methods, and models in detecting spam content on social media. Additionally, this paper also discussed spam content on Indonesian social media and provided comprehensive suggestions for possible implementation, further research direction, and a possible new approach. Discussions on the approach, research media, dataset, feature extraction & selection, the language, context-based or not, the algorithm, performance, future research direction, and challenges were carried out. This research compared the latest approaches and methods to see the gaps between these studies. Literature data are collected from 2015 to 2021 based on seven journal repository databases and filtered into 69 main articles. This paper aimed to conduct a comprehensive literature review for "spam content detection" to identify the various approaches taken and generate up to date issues, especially in the social media case study. Spam content detection is different from spammers' detection and thus requires a different approach. The spam content detection problem is still challenging due to its complexity, feature extraction process, language, context-aware detection capabilities, performance, and evaluation method.