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 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.

Opinion mining technique for developing student feedback analysis system using lexicon-based approach (OMFeedback)

2019 ◽  
Vol 25 (4) ◽  
pp. 2549-2560 ◽  
Author(s):  
Muslihah Wook ◽  
Noor Afiza Mat Razali ◽  
Suzaimah Ramli ◽  
Norshahriah Abdul Wahab ◽  
Nor Asiakin Hasbullah ◽  
...  
Keyword(s):  
Opinion Mining ◽  
Student Feedback ◽  
Feedback Analysis ◽  
Mining Technique ◽  
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.

scholarly journals Learn to Select via Hierarchical Gate Mechanism for Aspect-Based Sentiment Analysis

2019 ◽  
Author(s):  
Xiangying Ran ◽  
Yuanyuan Pan ◽  
Wei Sun ◽  
Chongjun Wang
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Original Sequence ◽  
Fine Grained ◽  
Complex Sentences ◽  
Specific Memory ◽  
Related Part ◽  
Memory Network ◽  
The Given

Aspect-based sentiment analysis (ABSA) is a fine-grained task. Recurrent Neural Network (RNN) model armed with attention mechanism seems a natural fit for this task, and actually it achieves the state-of-the-art performance recently. However, previous attention mechanisms proposed for ABSA may attend irrelevant words and thus downgrade the performance, especially when dealing with long and complex sentences with multiple aspects. In this paper, we propose a novel architecture named Hierarchical Gate Memory Network (HGMN) for ABSA: firstly, we employ the proposed hierarchical gate mechanism to learn to select the related part about the given aspect, which can keep the original sequence structure of sentence at the same time. After that, we apply Convolutional Neural Network (CNN) on the final aspect-specific memory. We conduct extensive experiments on the SemEval 2014 and Twitter dataset, and results demonstrate that our model outperforms attention based state-of-the-art baselines.

scholarly journals Embedding Projection for Targeted Cross-lingual Sentiment: Model Comparisons and a Real-World Study

2019 ◽  
Vol 66 ◽  
Author(s):  
Jeremy Barnes ◽  
Roman Klinger
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Target Language ◽  
Test Machine ◽  
Fine Grained ◽  
Sentence Level ◽  
Level Information ◽  
Cross Lingual ◽  
Multiple Domains ◽  
Embedding Methods

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

2021 ◽  
pp. 1-21
Author(s):  
Siyu Zhu ◽  
Jin Qi ◽  
Jie Hu ◽  
Haiqing Huang
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
House Of Quality ◽  
Customer Preference ◽  
Fine Grained ◽  
Customer Preferences ◽  
Analysis Methods ◽  
System Information ◽  
Increasing Demand

Abstract With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.

scholarly journals Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

2018 ◽  
Vol 9 (1) ◽  
pp. 92 ◽  
Author(s):  
Yili Wang ◽  
Hee Yong Youn
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Rapid Growth ◽  
State Of The Art ◽  
Feature Weighting ◽  
Electronic Text ◽  
Chi Square ◽  
Social Networking Services ◽  
Fine Grained ◽  
Twitter Data

The rapid growth in social networking services has led to the generation of a massivevolume of opinionated information in the form of electronic text. As a result, the research on textsentiment analysis has drawn a great deal of interest. In this paper a novel feature weighting approachis proposed for the sentiment analysis of Twitter data. It properly measures the relative significanceof each feature regarding both intra-category and intra-category distribution. A new statistical modelcalled Category Discriminative Strength is introduced to characterize the discriminability of thefeatures among various categories, and a modified Chi-square (2)-based measure is employed tomeasure the intra-category dependency of the features. Moreover, a fine-grained feature clusteringstrategy is proposed to maximize the accuracy of the analysis. Extensive experiments demonstrate thatthe proposed approach significantly outperforms four state-of-the-art sentiment analysis techniquesin terms of accuracy, precision, recall, and F1 measure with various sizes and patterns of training andtest datasets.

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