TwiInsight: Discovering Topics and Sentiments from Social Media Datasets

Learning to Generate Reviews and Discovering Sentiment

We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong basel…

Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network

Most recent existing aspect-term level sentiment analysis (ATSA) approaches combined various neural network models with delicately carved attention mechanisms built upon given aspect and context to generate refined sentence representations for better predictions. In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However, the annotated aspect term might be unavailable in real-world scenarios which may challenge the applicability of the existing methods. In this paper, we aim to improve …

Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon

Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion…

iACOS：利用信息丰富的自适应负样本推进隐式情感抽取

iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples

针对ABSA中含隐式方面与观点的四元组抽取研究不足的问题，提出iACOS方法，通过添加隐式标记、序列标注、多头注意力多标签分类及自适应负样本多任务学习，在两个基准数据集上F1分数显著优于现有基线。

Semantic Properties of Customer Sentiment in Tweets

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinion…

发现高影响力捷径推理：一种无模板的自动化方法

Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free Approach

针对现有捷径推理识别方法缺乏严重程度量化、易遗漏类型的问题，提出一种无模板自动化方法，利用分布外数据量化严重程度，在两类NLP任务上成功发现已知与未知捷径推理。