Sentiment analysis tends to use automated approaches to mine the sentiment information expressed in text, such as reviews, blogs and forum discussions. As most traditional approaches for sentiment analysis are based on supervised learning models and need many labeled corpora as their training data which are not always easily obtained, various unsupervised models based on Latent Dirichlet Allocation (LDA) have been proposed for sentiment classification.
In this paper, we propose a novel probabilistic modeling framework based on LDA, called Dependency-Topic-Affects-Sentiment-LDA (DTAS) model, which drops the "bag of words" assumption and assumes that the topics of sentences in a document form a Markov chain, and the sentiment of one sentence is affected by its corresponding topic and its previous sentence's topic. We applied DTAS to reviews of books and hotels. The experiment results of sentiment classification shows that DTAS outperforms other unsupervised generative models and gets high and stable accuracy.
|Title of host publication||Proceedings - 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|Publication status||Published - 12 Dec 2014|
|Event||26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014 - Limassol, Limassol, Cyprus|
Duration: 10 Nov 2014 → 12 Nov 2014
|Name||Proceedings-International Conference on Tools With Artificial Intelligence|
|Publisher||IEEE COMPUTER SOC|
|Conference||26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014|
|Period||10/11/14 → 12/11/14|