A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews

Balakrishnan, Vimala; Lok, Pik Yin; Rahim, Hajar Abdul

doi:10.1007/s11227-020-03412-w

Cited by 19 publications

(12 citation statements)

References 32 publications

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“…The machine learning algorithms are Support Vector Machine, Random Forest, and Naïve Bayes. Random Forest yielded the best accuracy for sentiment (F1 score = 73.8%; Kappa Cohen = 52.2%) and emotion (F1 score = 58.8%; Kappa Cohen = 44.7%) [22]. The architecture of the model used is shown in Fig.…”

Section: A Similar Research On Semi-supervised Text Annotationmentioning

confidence: 99%

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Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Cahyana¹,

Saifullah²,

Fauziah³

et al. 2022

IJACSA

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Sentiment analysis can detect hate speech using the Natural Language Processing (NLP) concept. This process requires annotation of the text in the labeling. However, when carried out by people, this process must use experts in the field of hate speech, so there is no subjectivity. In addition, if processed by humans, it will take a long time and allow errors in the annotation process for extensive data. To solve this problem, we propose an automatic annotation process with the concept of semi-supervised learning using the K-Nearest Neighbor algorithm. This process requires feature extraction of term frequency-inverse document frequency (TF-IDF) to obtain optimal results. KNN and TF-IDF were able to annotate and increase the accuracy of < 2% from the initial iteration of 57.25% to 59.68% in detecting hate speech. This process can annotate the initial dataset of 13169 with the distribution of 80:20 of training and testing data. There are 2370 labeled datasets; for testing, there are 1317 unannotated data; after preprocessing, there are 9482. The final results of the KNN and TF-IDF annotation processes have a length of 11235 for annotated data.

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Section: A Similar Research On Semi-supervised Text Annotationmentioning

confidence: 99%

“…2. The advantage of the approach used in [22] is that analyzing sentiment and emotion is carried out simultaneously. The downside is that the average accuracy for emotion classification based on Random Forest and SVM is around 61.3% (deficient), even though it uses a quite sophisticated algorithm.…”

Section: A Similar Research On Semi-supervised Text Annotationmentioning

confidence: 99%

Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Cahyana¹,

Saifullah²,

Fauziah³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…We used k = 10, hence 10 different models were trained and tested over 10 iterations before the final value is averaged. In classification approaches, it is common to use k = 5 or 10 [1,4,23].…”

Section: Methodsmentioning

confidence: 99%

“…These online reviews play a great role in influencing the purchasing decisions made by customers while providing more insights to the sellers. As online platforms including social media contain voluminous data, sentiment analysis provides an easy and fast mechanism to categorize the reviews, hence providing useful insights to both customers and sellers on the feedback of the products and services [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Sentiment analysis generally elicits a sentiment orientation (i.e., positive, neutral, negative) of textual information, which can improve decision-making processes for multitude domains including businesses such as finance and stock market [5][6][7], digital payment services [4], retails [2,8], and products [1,3,9], among others. Scholars investigating sentiment analysis based on textual communications have also examined or attempted to determine the sentiment ratings, often using scales ranging from 1 to 5 or 10 (i.e., higher scores indicate more positive reviews) [10].…”

Section: Introductionmentioning

confidence: 99%

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