A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook

doi:10.33168/jsms.2020.0310

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Furthermore, when compared to previous studies using manual labeling [21], [32], [33], [34], this study was able to obtain better accuracy using automatic labeling, which is the InSet lexicon. [35] in their study concluded that using the lexicon for automatic labeling is able to obtain better accuracy compared to manual labeling.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting

Aisy,

Prasetiyo

2023

Recursive J. of Informatics

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Abstract. The Indonesian Sexual Violence Law (TPKS Law) is a law that regulates forms of sexual violence. The TPKS Law reaped pros and cons in the drafting process and was officially ratified on April 12th, 2022. However, after being ratified, pros and cons can still be found and supervision is needed over the implementation of the law. Purpose: This study was conducted to identify the application and accuracy of soft voting on multinomial naïve Bayes and support vector machine algorithm, also to find out public opinion on the TPKS Law as a support tool in evaluating the law. Methods/Study design/approach: The method used is InSet lexicon for labeling with the soft voting classification method on the multinomial naive Bayes and support vector machine algorithm. Result/Findings: The accuracy obtained by applying 10 k-fold cross validation in soft voting is 84.31%, which uses a weight of 1:3 for multinomial naive Bayes and support vector machines. Soft voting obtains better accuracy than its standalone predictor, and also works well for sentiment analysis of the TPKS Law. Novelty/Originality/Value: This study using two combined lexicons (Colloquial Indonesian lexicon and the InaNLP formalization dictionary) in normalization process and using InSet lexicon as automatic labeling for sentiment analysis on TPKS Law.

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Section: Discussionmentioning

confidence: 99%

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting

Aisy,

Prasetiyo

2023

Recursive J. of Informatics

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“…To add on, Mahmood et al [21] employed a hybrid approach to classifying public opinion on social media in positive and negative sentiment, combining a lexicon-based with machine learning approaches such as Naïve Bayes and Support Vector Machines. The Support Vector Machine performs superior to the Naïve Bayes classifier, achieving an accuracy rate of 80% before combining with the lexicon-based approach, while the lexicon-based method alone reached 85%.…”

Section: Hybrid Approach In Sentiment Analysismentioning

confidence: 99%

Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review

Arief,

Samsudin

2023

IJACSA

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Sentiment analysis is crucial for businesses to understand customer reviews and assess sentiment polarity. A hybrid technique combining VADER and Multinomial LogisticRegression was used to analyze customer sentiment in online customer review data. VADER is a lexicon-based approach that labels reviews with sentiment using a predefined lexicon, whereas Multinomial Logistic Regression can determine the polarity of sentiment using VADER data. This study employed multiclass classification using TF-IDF vectorization to categorize sentiment as a positive, negative, or neutral class. Correctly managing neutral sentiments can assist businesses in identifying improvement opportunities. The utilization of the VADER lexicon and Multinomial Logistic Regression has been shown to significantly improve the performance of sentiment analysis in the context of multiclass classification problems. With a 75.213% accuracy rate, the VADER lexicon accurately recognizes neutral sentiment and is appropriate to adapt in categorizing sentiment related to customer reviews. Combined with Multinomial Logistic Regression, accuracy increases to 92.778%. In conclusion, the hybrid approach with VADER and Multinomial Logistic Regression can leverage the accuracy and reliability of multiclass customer sentiment analysis.

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“…Several studies and research have been carried out in the field of classification of Arabic texts, and more specifically in the automatic detection of hate speech in classical Arabic; Many of these studies have applied classic machine learning algorithms for tackling the task of classification including Support Vector Machine technique, and the Naï ve Bayes classifier (NB) [2], Decision Trees, K-Nearest Neighbor and other types of classifiers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

Hate Speech in the Arab Electronic Press and Social Networks

Awane¹,

Benlahmar²,

Falaki³

2021

RIA

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Nowadays we are witnessing an open world, characterized by globalization which is accompanied by a technology through which information circulates without borders, especially with the widespread use of social networking sites being the most common communication tool, that gives access through various applications to a large space for the presentation of multiple ideas, including extremist ideas, and the spread of hate speech. This paper introduces a system of detection of hate speech in the texts of Arabic read media and social media, which is based on a combined use of NLP, and machine learning methods. The training of the detection model is done on a large Dataset of articles, tweets and comments, collected, balanced and tokenized afterwards using BERT in Arabic. The trained model detects hate speech in Arabic and various Arabic based dialects, by classifying the texts into two classes: Neutral and Abusive. The above-mentioned model is evaluated using precision metrics, recall and f1 score, it has reached an accuracy of 83%.

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A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook

Cited by 6 publications

References 19 publications

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting

Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review

Hate Speech in the Arab Electronic Press and Social Networks

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