Unsupervised Fine-Grained Sentiment Analysis System Using Lexicons and Concepts

2014 ◽  
pp. 28-33 ◽  
Author(s):  
Nir Ofek ◽  
Lior Rokach
Keyword(s):  
Sentiment Analysis ◽  
Fine Grained ◽  
Analysis System

Opinion Mining and Sentiment Analysis

2017 ◽  
Author(s):  
Eric Breck ◽  
Claire Cardie
Keyword(s):  
Sentiment Analysis ◽  
Question Answering ◽  
Opinion Mining ◽  
Sports Fans ◽  
Fine Grained ◽  
Opinion Analysis ◽  
Opinion Summarization ◽  
Automatic Methods ◽  
Analysis System ◽  
Compositional Approaches

Opinions are ubiquitous in text, and readers of online text—from consumers to sports fans to news addicts to governments—can benefit from automatic methods that synthesize useful opinion-oriented information from the sea of data. In this chapter on opinion mining and sentiment analysis, we introduce an idealized, end-to-end opinion analysis system and describe its components. We present methods for classifying documents and text passages according to their sentiment as well as methods that perform more fine-grained extraction of opinion expressions, their holders and their targets. We also address supplementary tasks of opinion lexicon construction, opinion summarization, opinion-oriented question answering, multi-lingual sentiment analysis and compositional approaches to phrase-level sentiment analysis.

A Supervised Fine-Grained Sentiment Analysis System for Online Reviews

2015 ◽  
Vol 21 (4) ◽  
pp. 589-605 ◽  
Author(s):  
Hanxiao Shi ◽  
Wenping Zhan ◽  
Xiaojun Li
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Fine Grained ◽  
Analysis System

scholarly journals Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

2019 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Ikram Ullah ◽  
Shahab Shamshirband ◽  
Fazal Masud Kundi ◽  
Ammara Habib
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Student Feedback ◽  
Feedback Analysis ◽  
Fine Grained ◽  
Online Social Media ◽  
Logic Module ◽  
Data Collection And Analysis ◽  
Analysis System ◽  
Sentiment Score

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

scholarly journals MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 115 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Ehoche Faith
Keyword(s):  
Sentiment Analysis ◽  
Interaction Design ◽  
Mobile Application ◽  
Situational Awareness ◽  
Mobile Internet ◽  
The Public ◽  
Public Sentiment ◽  
High Altitude Area ◽  
Sentiment Dictionary ◽  
Analysis System

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Sign in / Sign up

Export Citation Format

Share Document