Ulster University Logo

Unsupervised Sentiment Classification: A Hybrid Sentiment-Topic Model Approach

Blair, Stuart, Bi, Yaxin and Mulvenna, Maurice (2017) Unsupervised Sentiment Classification: A Hybrid Sentiment-Topic Model Approach. In: The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2017. IEEE publisher. 8 pp. [Conference contribution]

[img] Text - Accepted Version
345kB
[img] Text - Supplemental Material
Indefinitely restricted to Repository staff only.

51kB

Abstract

With the large volume of text available online it is becoming impractical to use supervised machine learning methods that require a sizeable training set of labelled data. In this paper we introduced a new sentiment-topic model called the hybrid sentiment-topic model (HST). The HST model is a completely unsupervised sentiment classification method that allows for the topical context of words in documents to be accounted for when classifying sentiment. The only input needed for the model is a list of positive seed words, a list of negative seed words, and the number of topics. The HST model differs from similar models as it ensures that each objective topic discovered has both a positive sentiment-topic and negative sentiment-topic associated with it; other similar models do not guarantee symmetric sentiment-topics. The HST model performs three functions, firstly, it discovers objective topics in a corpus of text; secondly, it finds a positive and negative sentiment- topic for each objective topic; and finally, it performs sentiment classification. The HST model is tested using a dataset consisting of movie reviews and a dataset of social media posts. For each dataset a variety of seed word lists and different numbers of topics are tested; the HST model is then compared against similar sentiment-topic models. In all experiments conducted, the HST model was found to outperform similar sentiment-topic models in terms of classification accuracy by a noticeable margin. Additionally, the HST model was found to converge faster than similar models and during the generative process the accuracy was found to be more stable.

Item Type:Conference contribution (Paper)
Keywords:Unsupervised machine learning, Sentiment Classification, Topic Model
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:38944
Deposited By: Dr Yaxin Bi
Deposited On:01 Nov 2017 11:14
Last Modified:08 Nov 2018 23:23

Repository Staff Only: item control page