A distributed learning based sentiment analysis methods with Web applications

2022 ◽  
Author(s):  
Guanghao Xiong ◽  
Ke Yan ◽  
Xiaokang Zhou
Keyword(s):  
Sentiment Analysis ◽  
Web Applications ◽  
Distributed Learning ◽  
Analysis Methods

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

On the combination of "off-the-shelf" sentiment analysis methods

2016 ◽  
Author(s):  
Pollyanna Gonçalves ◽  
Daniel Hasan Dalip ◽  
Helen Costa ◽  
Marcos André Gonçalves ◽  
Fabrício Benevenuto
Keyword(s):  
Sentiment Analysis ◽  
Analysis Methods

scholarly journals Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Andrew J Reagan ◽  
Christopher M Danforth ◽  
Brian Tivnan ◽  
Jake Ryland Williams ◽  
Peter Sheridan Dodds
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Analysis Methods

scholarly journals Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guangyao Pang ◽  
Keda Lu ◽  
Xiaoying Zhu ◽  
Jie He ◽  
Zhiyi Mo ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rapid Development ◽  
Auxiliary Information ◽  
Location Model ◽  
Analysis Approach ◽  
Feature Location ◽  
Feature Extraction Method ◽  
Fine Grained ◽  
Analysis Methods ◽  
Benchmark Datasets

With the rapid development of Internet social platforms, buyer shows (such as comment text) have become an important basis for consumers to understand products and purchase decisions. The early sentiment analysis methods were mainly text-level and sentence-level, which believed that a text had only one sentiment. This phenomenon will cover up the details, and it is difficult to reflect people’s fine-grained and comprehensive sentiments fully, leading to people’s wrong decisions. Obviously, aspect-level sentiment analysis can obtain a more comprehensive sentiment classification by mining the sentiment tendencies of different aspects in the comment text. However, the existing aspect-level sentiment analysis methods mainly focus on attention mechanism and recurrent neural network. They lack emotional sensitivity to the position of aspect words and tend to ignore long-term dependencies. In order to solve this problem, on the basis of Bidirectional Encoder Representations from Transformers (BERT), this paper proposes an effective aspect-level sentiment analysis approach (ALM-BERT) by constructing an aspect feature location model. Specifically, we use the pretrained BERT model first to mine more aspect-level auxiliary information from the comment context. Secondly, for the sake of learning the expression features of aspect words and the interactive information of aspect words’ context, we construct an aspect-based sentiment feature extraction method. Finally, we construct evaluation experiments on three benchmark datasets. The experimental results show that the aspect-level sentiment analysis performance of the ALM-BERT approach proposed in this paper is significantly better than other comparison methods.

scholarly journals SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Filipe N Ribeiro ◽  
Matheus Araújo ◽  
Pollyanna Gonçalves ◽  
Marcos André Gonçalves ◽  
Fabrício Benevenuto
Keyword(s):  
Sentiment Analysis ◽  
Analysis Methods

Web-Based Traffic Sentiment Analysis: Methods and Applications

2014 ◽  
Vol 15 (2) ◽  
pp. 844-853 ◽  
Author(s):  
Jianping Cao ◽  
Ke Zeng ◽  
Hui Wang ◽  
Jiajun Cheng ◽  
Fengcai Qiao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Web Based ◽  
Analysis Methods

scholarly journals Analisis Sentimen Dan Topic Modelling Dalam Aplikasi Ruangguru

2020 ◽  
Vol 4 (1) ◽  
pp. 66
Author(s):  
Muhammad Romy Firdaus ◽  
Fikri Muhammad Rizki ◽  
Favian Muhammad Gaus ◽  
Indra Kusumajati Susanto
Keyword(s):  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Topic Modelling ◽  
Educational Content ◽  
Learning Space ◽  
Level Of Education ◽  
Important Thing ◽  
Online Tutoring ◽  
Analysis Methods

This study aims to determine and analyze responses regarding customer satisfaction Ruangguru Application to the learning space features in the Ruangguru Application at every level of education. This is useful to know the strengths and weaknesses of the Ruangguru Application based on sentiment responses from Ruangguru users. Ruangguru is an online tutoring startup and a technology-based educational content service and provider no. 1 in Indonesia. So of course, customer satisfaction is an important thing that is the goal of the company. So that when customer satisfaction is met, that is where the company can realize their goals. To see how the level of customer satisfaction, sentiment analysis methods and topic modeling are used in processing the data so that responses can be seen as to what is provided by the customer so that it can be an evaluation for the Ruangguru Application.

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