UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis

@article{Nguyen2018UITVSFCVS,
  title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis},
  author={Kiet Van Nguyen and Vu Duc Nguyen and Phu X. V. Nguyen and Tham T. H. Truong and Ngan Luu-Thuy Nguyen},
  journal={2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
  year={2018},
  pages={19-24}
}
Students’ feedback is a vital resource for the interdisciplinary research combining of two fields: sentiment analysis and education. [] Key Method The resource consists of over 16,000 sentences which are human-annotated on the two tasks. To assess the quality of our corpus, we measure the inter-annotator agreements and classification accuracies on our UIT-VSFC. As a result, we achieved 91.20% of the inter-annotator agreement for the sentiment-based task and 71.07% of that for the topic-based task. In…

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References

SHOWING 1-10 OF 20 REFERENCES

Mining Vietnamese Comparative Sentences for Sentiment Analysis

This paper presents a general framework for mining Vietnamese comparative sentences, and introduces a new corpus for the task in Vietnamese, and conducts a series of experiments on that corpus to investigate thetask in both linguistic and modeling aspects.

Sentiment Analysis for Vietnamese

  • Binh Thanh KieuS. Pham
  • Computer Science
    2010 Second International Conference on Knowledge and Systems Engineering
  • 2010
This paper addresses the problem at the sentence level and builds a rule-based system using the Gate framework, the first work that analyzes sentiment at sentence level in Vietnamese.

An empirical study on sentiment analysis for Vietnamese

An empirical study on machine learning based sentiment analysis for Vietnamese focuses on the task of sentiment classification, and introduces an annotated corpus for sentiment classification extracted from hotel reviews in Vietnamese.

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

A novel solution to target-oriented sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as “tweets” is introduced and it is shown that the hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarizing system.

SemEval-2013 Task 2: Sentiment Analysis in Twitter

Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask.

SemEval-2016 Task 4: Sentiment Analysis in Twitter

The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams.

SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining

This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.

SentBuk: Sentiment analysis for e-learning environments

SentBuk is a Facebook application that extracts information about the user sentiment automatically, in a non-intrusive way, so that adaptive e-learning systems can adapt any of their aspects according to each student sentiment, among other criteria.

Sentiment analysis in Facebook and its application to e-learning

Annotating Expressions of Opinions and Emotions in Language

The manual annotation process and the results of an inter-annotator agreement study on a 10,000-sentence corpus of articles drawn from the world press are presented.