Implementation of Naive Bayes Algorithm for Spam Comments Classification on Instagram

Priyoko, Beta; Yaqin, Ainul

doi:10.1109/icoiact46704.2019.8938575

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Various algorithms such as Decision Tree (DT) [26], Naive Bayes (NB) [27], Support Vector Machine (SVM) [28], K-Nearest Neighbor (KNN) [29], and Logistic Regression (LR) [30] are widely used in human activity recognition. Some of these algorithms were used beside Linear Discriminant Analysis (LDA) [31] and Ensemble Learning (EL) [32] to recognize activities of elderly people who are living alone as proposed in [7].…”

Section: ) Human Activity Recognitionmentioning

confidence: 99%

Future Activities Prediction Framework in Smart Homes Environment

2022

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Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents' behavior to provide residents with better and optimized services. A popular example is to analyze residents' behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents' next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%.

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Section: ) Human Activity Recognitionmentioning

confidence: 99%

Future Activities Prediction Framework in Smart Homes Environment

2022

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“…In the data acquisition stage, IG was chosen because 1) IG has a lot of spam comments, especially on Indonesian public figure accounts [33], [34]. 2) Posting and commenting on IG is in pairs suitable for the pair dataset; 3) IG has a lot of nonformal posts and comments, and it also contains a lot of emojis; 4) IG does not have a spam filtering feature in Indonesian yet.…”

Section: A Data Acquisitionmentioning

confidence: 99%

SPAMID-PAIR: A Novel Indonesian Post–Comment Pairs Dataset Containing Emoji

Chrismanto¹,

Sari²,

Suyanto³

2022

IJACSA

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The detection of spam content is an important task especially in social media. It has become a topic to be continuely studied in Natural Language Processing (NLP) area in the last few years. However, limited data sets are available for this research topic because most researchers collect the data by themselves and make it private. Moreover, most available data sets only provide the post content without considering the comment content. This data becomes a limitation because the post-comment pair is needed when determining the context of a comment from a particular post. The context may contribute to the decision of whether a comment is a spam or not. The scarcity of non-English data sets, including Indonesian, is also another issue. To solve these problems, the authors introduce SPAMID-PAIR, a novel post-comment pair data set collected from Instagram (IG) in Indonesian. It is collected from selected 13 Indonesian actress/actor accounts, each of which has more than 15 million followers. It contains 72874 pairs of data. This data set has been annotated with spam/non-spam labels in Unicode (UTF-8) text format. The data also includes a lot of emojis/emoticons from IG. To test the baseline performance, the data is tested with some machine learning methods using several scenarios and achieves good performance. This dataset aims to be used for the replicable experiment in spam content detection on social media and other tasks in the NLP area.

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“…Researchers have researched spam comments detection in IG previously, most in English and some in other languages, including Indonesia [7], [8], [9], [10], [11], [5]. In the previous research, the authors tried to develop a spam comment detection service on IG based on the REST web service [12].…”

Section: Introductionmentioning

confidence: 99%

“…Not much research has been done on detecting spam comments on IG in the Indonesian language. NB algorithm uses the classification method and has an accuracy of 72% [8]. In contrast, the opposite NB algorithm, namely Complementary Naïve Bayes (CNB) in an unbalanced dataset (non-spam comments outnumber spam comments) between spam and nonspam comments, has better accuracy [9].…”

Section: Introductionmentioning

confidence: 99%

Spam Comments Detection on Instagram Using Machine Learning and Deep Learning Methods

Chrismanto

Afiahayati

Sari

et al. 2022

LKJITI

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The more popular a public figure on Instagram (IG), the number of followers also increase. When a public figure posts something, there are many comments from other users. In fact, from all the comments, not all of them are relevant to the post, such as advertising, links, or clickbait comments. The type of comments that are irrelevant to the post is usually called spam comments. Spam comments will interfere with information flow and may lead to misleading information. This research compares machine learning (ML) and deep learning (DL) classification methods based on our collected Indonesian IG spam comment dataset. This research was conducted in the following steps: dataset preparation, pre-processing, simple normalization, features generation using TF-IDF and word embedding, application of ML and DL classification methods, performance evaluation, and comparison. The authors compare accuracy, F-1, precision, and recall from ML and DL results. This research shows that ML and DL methods do not significantly differ. The Linear SVM, Extreme Tree (ET), Regression, and Stochastics Gradient Descent algorithms can reach the accuracy of 0.93. At the same time, the DL method has the highest accuracy of 0.94 using the SimpleTransformer BERT architecture. The difference between ML and DL methods is not significantly different.

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Implementation of Naive Bayes Algorithm for Spam Comments Classification on Instagram

Cited by 6 publications

References 6 publications

Future Activities Prediction Framework in Smart Homes Environment

Future Activities Prediction Framework in Smart Homes Environment

SPAMID-PAIR: A Novel Indonesian Post–Comment Pairs Dataset Containing Emoji

Spam Comments Detection on Instagram Using Machine Learning and Deep Learning Methods

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