Sentence-Level Sentiment Analysis in the Presence of Modalities

2014 ◽  
pp. 1-16 ◽  
Author(s):  
Yang Liu ◽  
Xiaohui Yu ◽  
Bing Liu ◽  
Zhongshuai Chen
Keyword(s):  
Sentiment Analysis ◽  
Sentence Level

Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

2021 ◽  
Vol 20 (4) ◽  
pp. 1-22
Author(s):  
Dang Van Thin ◽  
Ngan Luu-Thuy Nguyen ◽  
Tri Minh Truong ◽  
Lac Si Le ◽  
Duy Tin Vo
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Network Architectures ◽  
Low Resource ◽  
The Neural Network ◽  
Sentence Level ◽  
Push Forward ◽  
Polarity Classification ◽  
Learning Architectures ◽  
Single Approach

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

Classification of sentence level sentiment analysis using cloud machine learning techniques

2017 ◽  
Vol 22 (S1) ◽  
pp. 1199-1209 ◽  
Author(s):  
R. Arulmurugan ◽  
K. R. Sabarmathi ◽  
H. Anandakumar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Sentence Level

scholarly journals Working Paper: Application of automatic sentiment analysis: The role of emotions in an experimental learning intervention

2020 ◽  
Author(s):  
Manyu Li
Keyword(s):  
Sentiment Analysis ◽  
School Achievement ◽  
Emotional Valence ◽  
Growth Mindset ◽  
Intervention Effect ◽  
Sentence Level ◽  
Academic Settings ◽  
Growth Mindset Intervention ◽  
Mindset Intervention

This secondary-analysis register report aims at testing the role of emotion in the intervention effect of an experimental intervention study in academic settings. Previous analyses of the National Study of the Learning Mindset (Yeager et al., 2019) showed that in a randomized controlled trial, high school students who were given the growth mindset intervention had, on average higher GPA than did students in the control condition. Previous analyses also showed that school achievement levels moderated the intervention effect. This study further explores whether the emotion students experienced during the growth mindset intervention plays a role in the intervention effect. Specifically, using a sentence-level automated text analysis for emotional valence (i.e. sentiment analysis), students’ written reflections during the intervention are analyzed. Linear mixed models are conducted to test if valence reflected in the written texts predicted higher intervention effect (i.e. higher post-intervention GPA given pre-intervention GPA). The moderating role of school achievement levels was also examined. A 10% random sample of the data was analyzed as a pilot study for this registered report to test for feasibility and proof-of-concept. Results of the pilot data showed small, yet significant relations between emotional valence and intervention effects. The results of this study have implications on the role of emotion in the results of intervention or experimental studies, especially those that are conducted in academic settings. This study also introduces a user-friendly text-based analytic method for experimental psychologists to detect and analyze sentence-level emotional valence in an intervention or experimental study.

scholarly journals A comparative study of machine translation for multilingual sentence-level sentiment analysis

2020 ◽  
Vol 512 ◽  
pp. 1078-1102 ◽  
Author(s):  
Matheus Araújo ◽  
Adriano Pereira ◽  
Fabrício Benevenuto
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Machine Translation ◽  
Sentence Level

Sentiment annotations for reviews: an information quality perspective

2018 ◽  
Vol 42 (5) ◽  
pp. 579-594 ◽  
Author(s):  
Heng-Li Yang ◽  
August F.Y. Chao
Keyword(s):  
Sentiment Analysis ◽  
Information Quality ◽  
Future Research ◽  
Content Type ◽  
Sentence Level ◽  
Product Service ◽  
Alternative Approach ◽  
Internet Information ◽  
Negative Sentiment ◽  
Practical Implications

Purpose The purpose of this paper is to propose sentiment annotation at sentence level to reduce information overloading while reading product/service reviews in the internet. Design/methodology/approach The keyword-based sentiment analysis is applied for highlighting review sentences. An experiment is conducted for demonstrating its effectiveness. Findings A prototype is built for highlighting tourism review sentences in Chinese with positive or negative sentiment polarity. An experiment results indicates that sentiment annotation can increase information quality and user’s intention to read tourism reviews. Research limitations/implications This study has made two major contributions: proposing the approach of adding sentiment annotation at sentence level of review texts for assisting decision-making; validating the relationships among the information quality constructs. However, in this study, sentiment analysis was conducted on a limited corpus; future research may try a larger corpus. Besides, the annotation system was built on the tourism data. Future studies might try to apply to other areas. Practical implications If the proposed annotation systems become popular, both tourists and attraction providers would obtain benefits. In this era of smart tourism, tourists could browse through the huge amount of internet information more quickly. Attraction providers could understand what are the strengths and weaknesses of their facilities more easily. The application of this sentiment analysis is possible for other languages, especially for non-spaced languages. Originality/value Facing large amounts of data, past researchers were engaged in automatically constructing a compact yet meaningful abstraction of the texts. However, users have different positions and purposes. This study proposes an alternative approach to add sentiment annotation at sentence level for assisting users.

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