Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification

Text representation can aid machines in understanding text. Previous work on text representation often focuses on the so-called forward implication, i.e., preceding words are taken as the context of later words for creating representations, thus ignoring the fact that the semantics of a text segment is a product of the mutual implication of words in the text: later words contribute to the meaning of preceding words. We introduce the concept of interaction and propose a two-perspective interaction representation, that encapsulates a local and a global interaction representation. Here, a local interaction representation is one that interacts among words with parent-children relationships on the syntactic trees and a global interaction interpretation is one that interacts among all the words in a sentence. We combine the two interaction representations to develop a Hybrid Interaction Representation (HIR). Inspired by existing feature-based and fine-tuning-based pretrain-finetuning approaches to language models, we integrate the advantages of feature-based and fine-tuning-based methods to propose the Pre-train, Interact, Fine-tune (PIF) architecture. We evaluate our proposed models on five widely-used datasets for text classification tasks. Our ensemble method, outperforms state-of-the-art baselines with improvements ranging from 2.03% to 3.15% in terms of error rate. In addition, we find that, the improvements of PIF against most state-of-the-art methods is not affected by increasing of the length of the text.Comment: 32 pages, 5 figure

Analyzing fluctuation of topics and public sentiment through social media data

Over the past decade years, Internet users were expending rapidly in the world. They form various online social networks through such Internet platforms as Twitter, Facebook and Instagram. These platforms provide a fast way that helps their users receive and disseminate information and express personal opinions in virtual space. When dealing with massive and chaotic social media data, how to accurately determine what events or concepts users are discussing is an interesting and important problem. This dissertation work mainly consists of two parts. First, this research pays attention to mining the hidden topics and user interest trend by analyzing real-world social media activities. Topic modeling and sentiment analysis methods are proposed to classify the social media posts into different sentiment classes and then discover the trend of sentiment based on different topics over time. The presented case study focuses on COVID-19 pandemic that started in 2019. A large amount of Twitter data is collected and used to discover the vaccine-related topics during the pre- and post-vaccine emergency use period. By using the proposed framework, 11 vaccine-related trend topics are discovered. Ultimately the discovered topics can be used to improve the readability of confusing messages about vaccines on social media and provide effective results to support policymakers in making their policy their informed decisions about public health. Second, using conventional topic models cannot deal with the sparsity problem of short text. A novel topic model, named Topic Noise based-Biterm Topic Model with FastText embeddings (TN-BTMF), is proposed to deal with this problem. Word co-occurrence patterns (i.e. biterms) are dirctly generated in BTM. A scoring method based on word co-occurrence and semantic similarity is proposed to detect noise biterms. In th