Soft Computing

, Volume 23, Issue 2, pp 599–611 | Cite as

An improved algorithm for sentiment analysis based on maximum entropy

  • Xin XieEmail author
  • Songlin Ge
  • Fengping Hu
  • Mingye Xie
  • Nan Jiang
Methodologies and Application


Sentiment analysis is an important field of study in natural language processing. In the massive data and irregular data, sentiment classification with high accuracy is a major challenge in sentiment analysis. To address this problem, a novel maximum entropy-PLSA model is proposed. In this model, we first use the probabilistic latent semantic analysis to extract the seed emotion words from the Wikipedia and the training corpus. Then features are extracted from these seed emotion words, which are the input of the maximum entropy model for training the maximum entropy model. The test set is processed similarly into the maximum entropy model for emotional classification. Meanwhile, the training set and the test set are divided by the K-fold method. The maximum entropy classification based on probabilistic latent semantic analysis uses important emotional classification features to classify words, such as the relevance of words and parts of speech in the context, the relevance with degree adverbs, the similarity with the benchmark emotional words and so on. The experiments prove that the classification method proposed by this paper outperforms the compared methods.


Semantic analysis Maximum entropy Probabilistic latent semantic analysis 



This work is supported by the National Natural Science Foundation, under Grant Nos. 61762037, 61640217, 41402290, 61462028, Science and Technology Support Program of Jiangxi Province, under Grant No. 20151BBE50055, and Science and Technology Project supported by education department of Jiangxi Province under Grant No. GJJ150541, and Nanchang City Sensor Network and Compressed Sensing Knowledge Innovation Team under Grant No. 2016T75.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Information EngineeringEast China Jiaotong UniversityNanchangPeople’s Republic of China
  2. 2.School of Civil EngineeringEast China Jiaotong UniversityNanchangPeople’s Republic of China
  3. 3.School of Information Science TechnologyEast China Normal UniversityShanghaiPeople’s Republic of China

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