Transfer learning has been widely used in natural language processing through deep pretrained language models, such as Bidirectional Encoder Representations from Transformers and Universal Sentence Encoder. Despite the great success, language models get overfitted when applied to small datasets and are prone to forgetting when fine-tuned with a classifier. To remedy this problem of forgetting in transferring deep pretrained language models from one domain to another domain, existing efforts explore fine-tuning methods to forget less. We propose DeepEmotex an effective sequential transfer learning method to detect emotion in text. To avoid forgetting problem, the fine-tuning step is instrumented by a large amount of emotion-labeled data collected from Twitter.We conduct an experimental study using both curated Twitter data sets and benchmark data sets. DeepEmotex models achieve over 91% accuracy for multi-class emotion classification on test dataset. We evaluate the performance of the fine-tuned Deep-Emotex models in classifying emotion in EmoInt and Stimulus benchmark datasets. The models correctly classify emotion in 73% of the instances in the benchmark datasets. The proposed DeepEmotex-BERT model outperforms Bi-LSTM result on the benchmark datasets by 23%. We also study the effect of the size of the fine-tuning dataset on the accuracy of our models. Our evaluation results show that fine-tuning with a large set of emotion-labeled data improves both the robustness and effectiveness of the resulting target task model.

We will demonstrate the visual analytics system V istreamT, that supports interactive mining of complex patterns within and across live data streams and stream pattern archives. Our system is equipped with both computational pattern mining and visualization techniques, which allow it to not only efficiently discover and manage patterns but also effectively convey the mining results to human analysts through visual displays. In our demonstration, we will illustrate that with V istreamT, analysts can easily submit, monitor and interact with a broad range of query types for pattern mining. This includes novel strategies for extracting complex patterns from streams in real time, summarizing neighbour-based patterns using multi-resolution compression strategies, selectively pushing patterns into the stream archive, validating the popularity or rarity of stream patterns by stream archive matching, and pattern evolution tracking to link patterns across time.

Software teams, as they communicate throughout the life-cycle of their projects, generate a substantial stream of textual data. Through emails and chats, developers discuss the requirements of their software system, they negotiate the distribution of tasks among them, and they make decisions about the system design, and the internal structure and functionalities of its code modules. The software research community has long recognized the importance and potential usefulness of such textual information. In this paper, we discuss our recent work on systematically analyzing several textual streams collected through our WikiDev2.0 tool. We use two different text-analysis methods to examine five different sources of textual data. We report on our experience using our method on analyzing the communications of a nine-member team over four months.