Enriched Latent Dirichlet Allocation for Sentiment Analysis

Abstract

One of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other parameters. In this study, three novel topic models called date sentiment LDA (DSLDA), author–date sentiment LDA (ADSLDA), and pack–author–date sentiment LDA (PADSLDA) are proposed. The proposed models extend LDA through some extra parameters such as date, author, helpfulness, sentiment, and subtopic. The proposed models use helpfulness in the Gibbs sampling algorithm. Helpfulness is a part of readers who found the review helpful. The proposed models divide the words into two categories: the words more affected by the distribution of subtopic and the words more affected by the main topic. In this study, a new concept called pack is introduced, and a new model called PADSLDA is proposed for sentiment analysis at pack level. The proposed models outperformed the baseline models because according to evaluations results, the extra parameters can appropriately affect the generating process of words in a review. Sentiment analysis at the document level, perplexity, and topic coherence are the main parameters used in the evaluations.

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

“…A smoothing count of 1 is included to avoid taking the logarithm of zero. In the present study, topic_coherency is computed through (18), equal to the average of top-ic_coherency values in Z. Furthermore, a higher value of topic_coherency reflects the better quality of the detected topics.…”

“…is observation shows that modeling the parameters weight and window improves sentiment classification at the documentlevel. According to (18), a lower topic_coherency value suggests that the retrieved subjects are of worse quality than one with a highertopic_coherency. e words in a subject accurately describe the subject and have a stronger association with one another.…”

“…LDA has motivated countless algorithms to expand to solve different problems [14][15][16][17]. In [18], the authors exhibit three topic models which make better LDA using date, helpfulness, and subtopic parameters. Articles [8,10,19] describe the methodology JST.…”

A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis

“…The neural network-based methods use the attention mechanism to assign different semantic weights to words with good experimental results in many downstream tasks, such as LSTM [6], BiLSTM [7] and BERT [8]. Semantic analysis based on attention mechanism has been involved in many works [9][10][11] and can reflect the different weights of words in different texts. The attention mechanism is introduced to obtain different weight of words in order to extract enough key information.…”

Proceedings of the 17th International Research Symposium on Service Excellence in Management - QUIS17

“…Each comment was assigned manually to up to three topics most representative of its content. We preferred manually curated resources over automatically generated (e.g., using topic-modelling techniques, such as latent Dirichlet allocation, LDA, Osmani, et al, 2020), because user-generated content, being informal, is prone to generate a lot of noise and the resulting categories are difficult to control and require additional curation.…”