S2SAN: A sentence-to-sentence attention network for sentiment analysis of online reviews

2021 ◽  
pp. 113603
Author(s):  
Ping Wang ◽  
Jiangnan Li ◽  
Jingrui Hou
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Attention Network

Aspect-based sentiment analysis for online reviews with hybrid attention networks

2021 ◽  
Author(s):  
Yuming Lin ◽  
Yu Fu ◽  
You Li ◽  
Guoyong Cai ◽  
Aoying Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Attention Networks

Classifying Sentiment Based on LDA Model

2013 ◽  
Vol 427-429 ◽  
pp. 2614-2617
Author(s):  
Qing Xi Peng
Keyword(s):  
Sentiment Analysis ◽  
Topic Models ◽  
Online Reviews ◽  
Simultaneous Analysis ◽  
Experimental Result ◽  
Proposed Model

Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.

Target-specific Sentiment Analysis of Dual-channel Interactive Attention Network

2021 ◽  
Author(s):  
Kai Wen ◽  
Xuhong Zhang ◽  
Na Huo ◽  
Yuhui Wan
Keyword(s):  
Sentiment Analysis ◽  
Attention Network ◽  
Dual Channel

scholarly journals A hybrid sentiment analysis approach using black widow optimization based feature selection

2021 ◽  
Author(s):  
Anand Joseph Daniel ◽  
◽  
M Janaki Meena ◽  
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Performance Metrics ◽  
Computation Time ◽  
Online Reviews ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Analysis Approach ◽  
Black Widow ◽  
Feature Selection Technique

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.

scholarly journals SentiProdBR: Building Domain-Specific Sentiment Lexicons for the Portuguese Language

2021 ◽  
Author(s):  
Tiago de Melo
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Bayes Theorem ◽  
Experimental Results ◽  
Product Categories ◽  
Domain Specific ◽  
Alternative Approaches ◽  
The Web

Online reviews are readily available on the Web and widely used for decision-making. However, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective methodology using probabilities of the Bayes’ Theorem for building a set of lexicons, called SentiProdBR, for 10 different product categories for the Portuguese language. Experimental results indicate that our methodology significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.

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