Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

Rajput, Quratulain; Haider, Sajjad; Ghani, Sayeed

doi:10.1155/2016/2385429

Cited by 90 publications

(40 citation statements)

References 20 publications

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“…First, in [14] proposed the system for analyzed and summarized the student feedbacks about each topic. According to [8] studied the course evaluation form student comments using sentiment dictionary to identify the frequency words and sentiment words, calculated the sentiment scores and represented with tag clouds. Moreover, [15] proposed the system for analyzed and summarized the student feedbacks from SMS and calculate the sentiment scores.…”

Section: Related Workmentioning

confidence: 99%

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Combined Approach for Teachers’ Evaluation Aspects Identification Using Dictionary and Patterns Based

Kaewyong¹,

Salim²,

Phang³

2019

IJIES

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Teacher performance evaluation is a common method and often used for evaluates teaching quality in higher education. With the rapid growth of opinion mining technique. Aspect-based opinion mining application has been possibly employed to extraction and summarization of students' comments for teacher evaluation. However, to automated teacher evaluation features identification from a large number of students' comments collection is very hard work. This study has the goal to address this problem. The main objectives of the proposed method are: (1) to identify teacher evaluation aspects, (2) to compare the efficiency of dictionary based, patterns based and the combination of them, and (3) to enhance the accuracy result in the teachers’ evaluation aspects identification from the unstructured text of students' feedbacks. The students' feedbacks were collected by questionnaires and the dataset was constructed manually with a total of 4,496 sentences from 300 undergraduate student responses in 10 subjects by purposive sampling and the collection of positive and negative sentences from 30 participants group interviewed in the workshop. Both approaches were applied to identify the frequency teachers' evaluation aspects. The experimental results found that our proposed approach provided reasonably more accurate results, the combination approach enhanced a significantly average of precision and recall. For future work, we focus on the application of new linguistic patterns and non-frequency aspects in order to increase the accuracy result. Keywords—aspects identification, lexicon relation, linguistic pattern, opinion mining, teacher evaluation.

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Section: Related Workmentioning

confidence: 99%

“…In recent year, sentiment analysis was grown up. Therefore, it has been widely used in the evaluation of products and services from customer reviews and it has been applying to the evaluation of political, tourism, medical and other areas [5], [6], [7], [8], [9]. However, with the rapid growth of sentiment analysis technique.…”

Section: Introductionmentioning

confidence: 99%

Combined Approach for Teachers’ Evaluation Aspects Identification Using Dictionary and Patterns Based

Kaewyong¹,

Salim²,

Phang³

2019

IJIES

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“…The method produces accurate classifications than previous algorithms. In [8], a sentiment analysis algorithm is presented which uses lexicon to analyze the performance of teachers. The method supports the evaluation of teacher's performance by obtaining feedback from students.…”

Section: Procedures For Paper Submissionmentioning

confidence: 99%

Text Mining with Topical and Informative Measures using Semantic Ontology

Shobarani¹,

Arulanandham²

2019

IJITEE

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The text mining has been identified as dominant throughout the era in many scientific problems. Number of techniques have been identified and proposed earlier, each uses different features towards mining text information. However, they suffer to achieve higher performance in mining relevant text information or documents. To support the development of text mining tasks, an efficient topical and informative measures based algorithm is presented, which uses semantic ontology and the taxonomy as dictionary. The text features of the documents has been extracted to generate set of terms. For any document, the text features are used to remove the noisy features like stop words, stemming and tagging. With the noise removed pure terms, the method estimates the Topical Depth Similarity (TDS) and informative Depth Similarity (IDM) measures. The measures has been estimated towards each document to perform text mining. The input query has been estimated for the TDS measure to identify the category of the query. According to the category of query, the method estimates the TDS and IDS measures to mining the text. The proposed method improve the performance of text mining with reduces false classification ratio.

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“…Unimodal SDA has been employed by a number of techniques (Medhat, Hassan, & Korashy, 2014). Text-based SDA has been applied to a broad set of online learning, such as online evaluation (Chauhan, Agrawal, & Meena, 2019), student feedback (Rajput, Haider, & Ghani, 2016), and online discussion (Hernández-Sellés, Muñoz-Carril, & González-Sanmamed, 2019). Today though, with diverse forms of interactions boomed by learning communities, a shift towards increasing multimodal sentiment information is indeed a good advancement for comprehensively capturing sentiments expressed by educational stakeholders.…”

mentioning

confidence: 99%

A review on sentiment discovery and analysis of educational big‐data

Han

Wu²,

Huang

et al. 2019

WIREs Data Min & Knowl

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Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education. For big educational data streams, SDA faces challenges in unimodal feature selection, sentiment classification, and multimodal fusion. As such, a large body of studies in the literature explores diverse approaches to SDA for educational applications. This paper provides a self‐contained, uniform overview of the SDA techniques for education. In particular, we focus on prominent studies in unimodal sentiment features and classifications (e.g., text, audio, and visual). In addition, we present a novel SDA framework of multimodal fusions, together with description of their crucial components. Based on this framework, we review different approaches to SDA on education from the perspectives of approaches and applications. After comprehensively reviewing the SDA techniques on education, we present the trends and prospectives of the future SDA research under ubiquitous education contexts. This article is categorized under: Application Areas > Education and Learning

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Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

Cited by 90 publications

References 20 publications

Combined Approach for Teachers’ Evaluation Aspects Identification Using Dictionary and Patterns Based

Combined Approach for Teachers’ Evaluation Aspects Identification Using Dictionary and Patterns Based

Text Mining with Topical and Informative Measures using Semantic Ontology

A review on sentiment discovery and analysis of educational big‐data

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