TY - GEN
T1 - Unsupervised sentiment analysis of twitter posts using density matrix representation
AU - Zhang, Yazhou
AU - Song, Dawei
AU - Li, Xiang
AU - Zhang, Peng
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Nowadays, a series of pioneering studies provide the evidence that quantum probability theory can be applied in information retrieval as a mathematical framework, such as Quantum Language Model (QLM) and its variants. In these studies, the density matrix, which is defined on the quantum probabilistic space, is used to represent query and document. However, these studies are only designed for information retrieval tasks, which are unable to model sentiment information. In this paper, we investigate the feasibility of quantum probability theory for twitter sentiment analysis, and propose a density matrix based unsupervised sentiment analysis approach. The main idea is to artificially create two sentiment dictionaries, generate density matrices of documents and dictionaries using an extended QLM, then employ the quantum relative entropy to judge the similarity between density matrices of documents and dictionaries. Extensive experiments are conducted on two widely used twitter datasets, which are the Obama-McCain Debate (OMD) dataset and Sentiment Strength Twitter Dataset (SS-Tweet). The experimental results show that our approach significantly outperforms a number of baselines, demonstrating the effectiveness of the proposed density matrix based sentiment analysis approach.
AB - Nowadays, a series of pioneering studies provide the evidence that quantum probability theory can be applied in information retrieval as a mathematical framework, such as Quantum Language Model (QLM) and its variants. In these studies, the density matrix, which is defined on the quantum probabilistic space, is used to represent query and document. However, these studies are only designed for information retrieval tasks, which are unable to model sentiment information. In this paper, we investigate the feasibility of quantum probability theory for twitter sentiment analysis, and propose a density matrix based unsupervised sentiment analysis approach. The main idea is to artificially create two sentiment dictionaries, generate density matrices of documents and dictionaries using an extended QLM, then employ the quantum relative entropy to judge the similarity between density matrices of documents and dictionaries. Extensive experiments are conducted on two widely used twitter datasets, which are the Obama-McCain Debate (OMD) dataset and Sentiment Strength Twitter Dataset (SS-Tweet). The experimental results show that our approach significantly outperforms a number of baselines, demonstrating the effectiveness of the proposed density matrix based sentiment analysis approach.
KW - Density matrix
KW - Quantum Language Model
KW - Sentiment analysis
UR - https://www.scopus.com/pages/publications/85044481909
U2 - 10.1007/978-3-319-76941-7_24
DO - 10.1007/978-3-319-76941-7_24
M3 - Conference contribution
AN - SCOPUS:85044481909
SN - 9783319769400
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 316
EP - 329
BT - Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings
A2 - Azzopardi, Leif
A2 - Pasi, Gabriella
A2 - Hanbury, Allan
A2 - Piwowarski, Benjamin
PB - Springer Verlag
T2 - 40th European Conference on Information Retrieval, ECIR 2018
Y2 - 26 March 2018 through 29 March 2018
ER -