Changliang Li

Recursive Deep Learning for Sentiment Analysis over Social Data

Sentiment analysis has now become a popular research problem to tackle in NLP field. However, there are very few researches conducted on sentiment analysis for Chinese. Progress is held back due to lack of large and labelled corpus and powerful models. To remedy this deficiency, we build a Chinese Sentiment Treebank over social data. It concludes 13550 labeled sentences which are from movie reviews. Furthermore, we introduce a novel Recursive Neural Deep Model (RNDM) to predict sentiment label based on recursive deep learning. We consider the problem of classifying one sentence by overall sentiment, determining a review is positive or negative. On predicting sentiment label at sentence level, our model outperforms other commonly used baselines, such as Naïve Bayes, Maximum Entropy and SVM, by a large margin.

Compositional Recurrent Neural Networks for Chinese Short Text Classification

Word segmentation is the first step in Chinese natural language processing, and the error caused by word segmentation can be transmitted to the whole system. In order to reduce the impact of word segmentation and improve the overall performance of Chinese short text classification system, we propose a hybrid model of character-level and word-level features based on recurrent neural network (RNN) with long short-term memory (LSTM). By integrating character-level feature into word-level feature, the missing semantic information by the error of word segmentation will be constructed, meanwhile the wrong semantic relevance will be reduced. The final feature representation is that it suppressed the error of word segmentation in the case of maintaining most of the semantic features of the sentence. The whole model is finally trained end-to-end with supervised Chinese short text classification task. Results demonstrate that the proposed model in this paper is able to represent Chinese short text effectively, and the performances of 32-class and 5-class categorization outperform some remarkable methods.

Parallel Recursive Deep Model for Sentiment Analysis

Sentiment analysis has now become a popular research problem to tackle in Artificial Intelligence (AI) and Natural Language Processing (NLP) field. We introduce a novel Parallel Recursive Deep Model (PRDM) for predicting sentiment label distributions. The main trait of our model is to not only use the composition units, i.e., the vector of word, phrase and sentiment label with them, but also exploit the information encoded among the structure of sentiment label, by introducing a sentiment Recursive Neural Network (sentiment-RNN) together with RNTN. The two parallel neural networks together compose of our novel deep model structure, in which Sentiment-RNN and RNTN cooperate with each other. On predicting sentiment label distributions task, our model outperforms previous state of the art approaches on both full sentences level and phrases level by a large margin.

Obtaining Better Word Representations via Language Transfer

Vector space word representations have gained big success recently at improving performance across various NLP tasks. However, existing word embeddings learning methods only utilize homo-lingual corpus. Inspired by transfer learning, we propose a novel language transfer method to obtain word embeddings via language transfer. Under this method, in order to obtain word embeddings of one language target language, we train models on corpus of another different language source language instead. And then we use the obtained source language word embeddings to represent target language word embeddings. We evaluate the word embeddings obtained by the proposed method on word similarity tasks across several benchmark datasets. And the results show that our method is surprisingly effective, outperforming competitive baselines by a large margin. Another benefit of our method is that the process of collecting new corpus might be skipped.

Characterizing emotion entrainment in social media

The sociological theory of entrainment accounts for the synchronization of human rhythmic modalities through social interactions: they coordinate in a variety of dimensions including linguistic styles, facial expressions, music pace, applause, and so on. Though highly relevant, emotion entrainment has received little attention to date. In addition, most previous studies on entrainment are done through small scale or controlled laboratory studies. In this paper, we investigate emotion entrainment in the context of online social media. To the best of our knowledge, this is the first time that emotion entrainment has been examined on a large scale, real world setting. For this purpose, we propose a framework that can model entrainment phenomenon and measure its effect. Our framework differentiates from previous research by its model-free essential and discerning in entrainment directions. These traits enable us to model entrainment dynamics under few assumptions, and distinguish emotion flow of entrainment. In our studies, we investigate entrainment patterns under different emotion states, i.e. positive, neutral and negative. We discover that entrainments under different emotions all follow a power law distribution. Besides, people are willing to entrain to others under positive emotion, and users with positive emotion are more likely to be entrained. By inspecting the interactions between entrainment and emotion, we reveal that entrainment has an effect of negotiating different emotion types toward an even distribution.

Modeling emotion entrainment of online users in emergency events

Emotion entrainment accounts for the rhythmic convergence of human emotions through social interactions. This phenomenon abounds in various disciplines, i.e. effervescency in soccer games, anger proliferation in violence incidents, or anxiety diffusion in disasters. Although emotion entrainment is highly relevant to the quality of human daily life, the principles underpinning this phenomenon is still unclear. Previous dynamic models try to explain entrainment phenomenon by assuming symmetrical coupling among identical individuals. Yet this assumption clearly does not hold in real-world human interactions. As such, we propose an alternative model that captures asymmetric relationships. In depicting the coupling mechanism, the effect of social influence is also encoded. Experimental results on two emergent social events suggest that the proposed model characterizes emotion trends with high accuracy. Also, we explain the emotion dynamics by analyzing the reconstructed entrainment matrix. Our work may present practical implications for those who want to guide or regulate the emotion evolution in emergency events discussed online.

Simulated Spoken Dialogue System Based on IOHMM with User History

Expanding corpora is very important in designing a spoken dialogue system (SDS). In this big data era, data is expensive to collect and there are rare annotations. Some researchers make much work to expand corpora, most of which is based on rule. This paper presents a probabilistic method to simulate dialogues between human and machine so as to expand a small corpus with more varied simulated dialogue acts. The method employs Input/output HMM with user history (UH-IOHMM) to learn system and user dialogue behavior. In addition, this paper compares with simulation system based on standard IOHMM. We perform experiments using the WDC-ICA corpus, weather domain corpus with annotation. And the experiment result shows that the method we present in this paper can produce high quality dialogue acts which are similar to real dialogue acts.

Hierarchical Hybrid Attention Networks for Chinese Conversation Topic Classification

Topic classification is useful for applications such as forensics analysis and cyber-crime investigation. To improve the overall performance on the task of Chinese conversation topic classification, we propose a hierarchical neural network with automatic semantic features selection, which is a hierarchical architecture that depicts the structure of conversations. The model firstly incorporates speaker information into the character- and word-level attentions and generates sentence representation, then uses attention-based BLSTM to construct the conversation representation. Experimental results on three datasets demonstrate that our model achieves better performance than multiple baselines. It indicates that the proposed architecture can capture the informative and salient features related to the meaning of a conversation for topic classification. And we release the dataset of this paper that can be obtained from https://github.com/njoe9/H-HANs.

Measuring Word Semantic Similarity Based on Transferred Vectors

Semantic similarity between words has now become a popular research problem to tackle in natural language processing (NLP) field. Word embedding have been demonstrated progress in measuring word similarity recently. However, limited to the distributional hypothesis, basic embedding methods generally have drawbacks in nature. One of the limitations is that word embeddings are usually by predicting a target word in its local context, leading to only limited information being captured. In this paper, we propose a novel transferred vectors approach to compute word semantic similarity. Transferred vectors are obtained via a reasonable combination of the source word and its nearest neighbors on semantic level. We conduct experiments on popular both English and Chinese benchmarks for measuring word similarity. The experiment results demonstrate that our method outperforms previous state-of-the-art by a large margin.

Constructing a Chinese Conversation Corpus for Sentiment Analysis

Sentiment analysis plays an important role in many applications. This paper introduces our ongoing work related to the sentiment analysis on Chinese conversation. The main purpose is to construct a Chinese conversation corpus for sentiment analysis and provide a benchmark result on this corpus. To explore the effectiveness of machine learning based approaches for sentiment analysis on Chinese conversation, we firstly collected conversational data from some online English learning websites and our instant messages, and manually annotated it with three sentiment polarities and 22 fine-grained emotion classes. Then we applied multiple representative classification methods to evaluate the corpus. The evaluation results provide good suggestions for the future research. And we will release the corpus with gold standards publicly for research purposes.