Panpan Wang

Bio: Panpan Wang is an academic researcher from Tianjin University. The author has contributed to research in topics: Sentiment analysis & Recurrent neural network. The author has an hindex of 8, co-authored 15 publications receiving 138 citations.

Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis.

Abstract: Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i.e., the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model.

A quantum-inspired sentiment representation model for twitter sentiment analysis

Abstract: Sentiment analysis aims to capture the diverse sentiment information expressed by authors in given natural language texts, and it has been a core research topic in many artificial intelligence areas. The existing machine-learning-based sentiment analysis approaches generally focus on employing popular textual feature representation methods, e.g., term frequency-inverse document frequency (tf-idf), n-gram features, and word embeddings, to construct vector representations of documents. These approaches can model rich syntactic and semantic information, but they largely fail to capture the sentiment information that is central to sentiment analysis. To address this issue, we propose a quantum-inspired sentiment representation (QSR) model. This model can not only represent the semantic content of documents but also capture the sentiment information. Specifically, since adjectives and adverbs are good indicators of subjective expression, this model first extracts sentiment phrases that match the designed sentiment patterns based on adjectives and adverbs. Then, both single words and sentiment phrases in the documents are modeled as a collection of projectors, which are finally encapsulated in density matrices through maximum likelihood estimation. These density matrices successfully integrate the sentiment information into the representations of documents. Extensive experiments are conducted on two widely used Twitter datasets, which are the Obama-McCain Debate (OMD) dataset and the Sentiment140 Twitter dataset. The experimental results show that our model significantly outperforms a number of state-of-the-art baselines and demonstrate the effectiveness of the QSR model for sentiment analysis.

Sentiment analysis using deep learning architectures: a review

Abstract: Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.

A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis

Abstract: Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU. PBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our proposed PBAN model.

Quantum cognition: A new theoretical approach to psychology

Abstract: What type of probability theory best describes the way humans make judgments under uncertainty and decisions under conflict? Although rational models of cognition have become prominent and have achieved much success, they adhere to the laws of classical probability theory despite the fact that human reasoning does not always conform to these laws. For this reason we have seen the recent emergence of models based on an alternative probabilistic framework drawn from quantum theory. These quantum models show promise in addressing cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory. This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach.