Multi-aspect and Multi-class Based Document Sentiment Analysis of Educational Data Catering Accreditation Process

Valakunde, N. D.; Patwardhan, Manasi

doi:10.1109/cube.2013.42

Cited by 29 publications

(17 citation statements)

References 17 publications

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“…Sentiment analysis is a task of identifying and extracting users' opinion about a given topic or a product into a positive, negative, and neutral polarity, score (ranking, aggregated), or star ratings. It can be performed at a document level [10], [11], sentence level [12], [13], topic level and aspect (feature) level [14], [15]. It can further be categorized based upon the techniques used, such as, lexicon-based [16]- [18], featuresbased [10], [19]- [21], those using conventional machine learning approaches, i.e., Naive Bayes (NB), SVM [18], and unsupervised methods [14], and more recently deep learningbased sentiment analysis [12], [22].…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

2020

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Students' feedback is an effective mechanism that provides valuable insights about teachinglearning process. Handling opinions of students expressed in reviews is a quite labour-intensive and tedious task as it is typically performed manually by the human intervention. While this task may be viable for smallscale courses that involve just a few students' feedback, it is unpractical for large-scale cases as it applies to online courses in general, and MOOCs, in particular. Therefore, to address this issue, we propose in this paper a framework to automatically analyzing opinions of students expressed in reviews. Specifically, the framework relies on aspect-level sentiment analysis and aims to automatically identify sentiment or opinion polarity expressed towards a given aspect related to the MOOC. The proposed framework takes advantage of weakly supervised annotation of MOOC-related aspects and propagates the weak supervision signal to effectively identify the aspect categories discussed in the unlabeled students' reviews. Consequently, it significantly reduces the need for manually annotated data which is the main bottleneck for all deep learning techniques. A large-scale real-world education dataset containing around 105k students' reviews collected from Coursera and a dataset comprising of 5989 students' feedback in traditional classroom settings are used to perform experiments. The experimental results indicate that our proposed framework attains inspiring performance with respect to both the aspect category identification and the aspect sentiment classification. Moreover, the results suggest that the framework leads to more accurate results than the expensive and labour-intensive sentiment analysis techniques relying heavily on manually labelled data.

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Section: Related Workmentioning

confidence: 99%

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

2020

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“…In [21], Akaichi utilized a combination of unigram, bigram, and trigram features and obtained 72.78% accuracy using the SVM. Valakunde and Patwardhan [28] aimed at five-class (e.g., strong positive, positive, neutral, negative, and strong negative) sentiment classification and obtained 81% accuracy using the SVM with bigram features. In the study of Gautam and Yadav [18], they utilized the SVM along with the semantic analysis model for the sentiment binary classification of Twitter texts and achieved 89.9% accuracy using unigram features.…”

Section: Machine Learning For Sentiment Classificationmentioning

confidence: 99%

Sentiment Classification Using Convolutional Neural Networks

2019

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As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

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“…Content mining bundle in "R" called "tm" [4] evacuates undesirable numbers and Punctuations shape content. Stemming [1,3] is the term utilized as a part of data recovery to portray the procedure for decreasing bent (or in some cases inferred) words to their root frame. The stems are favored over words serves two valuable part in assumption examination.…”

Section: Data Collectionmentioning

confidence: 99%

“…Thus, with the utilization of stem (root) word, sentence words are pleasantly contrasted and number of positive/negative words making it simple for investigation. Stop words [1,3], by and large known to be clamor words or most normal words, which don't illuminate any huge reason in the field of feeling arrangement. Stop words are the most widely recognized words in an any dialect, however there is no particular, single, unequivocal all inclusive rundown of them.…”

Section: Data Collectionmentioning

confidence: 99%

Design of Hybrid Method for Automatic Sentiment Classification System

2019

IJRTE

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The Developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that focuses on opinions and sentiments as a major aspect of their central goal. This work introduces hybrid approach that includes lexicon based approach and machine learning approach for extracting aspects and sentiments

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Multi-aspect and Multi-class Based Document Sentiment Analysis of Educational Data Catering Accreditation Process

Cited by 29 publications

References 17 publications

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

Sentiment Classification Using Convolutional Neural Networks

Design of Hybrid Method for Automatic Sentiment Classification System

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