Abstract
Emojis are being frequently used in todays digital world to express from simple to complex thoughts more than ever before. Hence, they are also being used in sentiment analysis and targeted marketing campaigns. In this work, we performed sentiment analysis of Tweets as well as on emoji dataset from the Kaggle. Since tweets are sentences we have used Universal Sentence Encoder (USE) and Sentence Bidirectional Encoder Representations from Transformers (SBERT) end-to-end sentence embedding models to generate the embeddings which are used to train the Standard fully connected Neural Networks (NN), and LSTM NN models. We observe the text classification accuracy was almost the same for both the models around 98 percent. On the contrary, when the validation set was built using emojis that were not present in the training set then the accuracy of both the models reduced drastically to 70 percent. In addition, the models were also trained using the distributed training approach instead of a traditional singlethreaded model for better scalability. Using the distributed training approach, we were able to reduce the run-time by roughly 15% without compromising on accuracy. Finally, as part of explainable AI the Shap algorithm was used to explain the model behaviour and check for model biases for the given feature set.
Abstract (translated)
在当今的数字世界中，表情符号（Emojis）被频繁地用于表达从简单到复杂的思想，比以往任何时候都更多。因此，它们也被应用于情感分析和定向市场营销活动中。在这项工作中，我们对来自Kaggle的推特数据集以及Emoji数据集进行了情感分析。由于推文是句子形式，我们使用了通用句子编码器（Universal Sentence Encoder, USE）及句向量双向变换模型（Sentence Bidirectional Encoder Representations from Transformers, SBERT）生成嵌入，并用于训练标准全连接神经网络（NN）和长短期记忆神经网络（LSTM NN）模型。我们观察到，对于这两种模型而言，文本分类的准确率几乎相同，约为98%。 然而，当使用不在训练集中的表情符号构建验证集时，两个模型的准确性都大幅下降至70%左右。此外，为了提高可扩展性，我们将分布式培训方法应用于这些模型，而不是传统的单线程模型进行训练。采用这种分布式训练方式后，我们在不牺牲准确性的前提下将运行时间减少了大约15%。 最后，在解释人工智能（Explainable AI）的框架内，我们使用Shap算法来解释模型行为，并检查在给定特征集下的模型偏见问题。
URL
https://arxiv.org/abs/2502.13278
