Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm

Abstract

Latent Dirichlet Allocation (LDA) is an approach to unsupervised learning that aims to investigate the semantics among words in a document as well as the influence of a subject on a word. As an LDA-based model, Joint Sentiment-Topic (JST) examines the impact of topics and emotions on words. The emotion parameter is insufficient, and additional parameters may play valuable roles in achieving better performance. In this study, two new topic models, Weighted Joint Sentiment-Topic (WJST) and Weighted Joint Sentiment-Topic 1 (WJST1), have been presented to extend and improve JST through two new parameters that can generate a sentiment dictionary. In the proposed methods, each word in a document affects its neighbors, and different words in the document may be affected simultaneously by several neighbor words. Therefore, proposed models consider the effect of words on each other, which, from our view, is an important factor and can increase the performance of baseline methods. Regarding evaluation results, the new parameters have an immense effect on model accuracy. While not requiring labeled data, the proposed methods are more accurate than discriminative models such as SVM and logistic regression in accordance with evaluation results. The proposed methods are simple with a low number of parameters. While providing a broad perception of connections between different words in documents of a single collection (single-domain) or multiple collections (multidomain), the proposed methods have prepared solutions for two different situations (single-domain and multidomain). WJST is suitable for multidomain datasets, and WJST1 is a version of WJST which is suitable for single-domain datasets. While being able to detect emotion at the level of the document, the proposed models improve the evaluation outcomes of the baseline approaches. Thirteen datasets with different sizes have been used in implementations. In this study, perplexity, opinion mining at the level of the document, and topic_coherency are employed for assessment. Also, a statistical test called Friedman test is used to check whether the results of the proposed models are statistically different from the results of other algorithms. As can be seen from results, the accuracy of proposed methods is above 80% for most of the datasets. WJST1 achieves the highest accuracy on Movie dataset with 97 percent, and WJST achieves the highest accuracy on Electronic dataset with 86 percent. The proposed models obtain better results compared to Adaptive Lexicon learning using Genetic Algorithm (ALGA), which employs an evolutionary approach to make an emotion dictionary. Results show that the proposed methods perform better with different topic number settings, especially for WJST1 with 97% accuracy at |Z| = 5 on the Movie dataset.

Mining LDA topics on construction engineering change risks based on graded evidence

“…LDA stands as a probabilistic and generative statistical model devised for the purpose of topic modeling. Originating from the collaborative efforts of David M. Blei, Andrew Y. Ng, and Michael I. Jordan in 2003 [ 23 ], LDA has found extensive application in natural language processing and text analysis, specifically aimed at unveiling latent topics embedded within a corpus of documents [ 24 ]. Over the years, the LDA model has become a cornerstone not only in literature knowledge mining [ 25 ] but has also demonstrated prowess in diverse domains such as topic discovery, Sentiment analysis [ 26 ] and topic evolution [ 27 ].…”

Untitled

“…LDA stands as a probabilistic and generative statistical model devised for the purpose of topic modeling. Originating from the collaborative efforts of David M. Blei, Andrew Y. Ng, and Michael I. Jordan in 2003 [23], LDA has found extensive application in natural language processing and text analysis, specifically aimed at unveiling latent topics embedded within a corpus of documents [24]. Over the years, the LDA model has become a cornerstone not only in…”

Untitled

“…LDA stands as a probabilistic and generative statistical model devised for the purpose of topic modeling. Originating from the collaborative efforts of David M. Blei, Andrew Y. Ng, and Michael I. Jordan in 2003 [23], LDA has found extensive application in natural language processing and text analysis, specifically aimed at unveiling latent topics embedded within a corpus of documents [24]. Over the years, the LDA model has become a cornerstone not only in…”