Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter

Amalia, Rizkiana; Bijaksana, Moch Arif; Darmantoro, Dhinta

doi:10.1088/1742-6596/971/1/012039

Cited by 19 publications

(14 citation statements)

References 11 publications

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“…Many works have explored the negation problem in detail in the English and other languages (Amalia, Bijaksana, & Darmantoro, 2018;Gautam, Maharjan, Banjade, Tamang, & Rus, 2018;Mittal, Agarwal, Chouhan, Bania, & Pareek, 2013;Villalba-Osornio, Pérez-Celis, Villasenor-Pineda, & Montes-y-Gómez;Zou, Zhu, & Zhou, 2015), whereas few studies have addressed this issue in the Arabic language as this field is still at an early stage. In this section, we explore how the negation problem has been addressed in previous studies of Arabic sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

Al-Harbi

2020

International Journal of Operations Research and Information Systems

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One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a review and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the authors address the negation problem in colloquial Arabic sentiment classification using the machine learning approach. To this end, they propose a simple rule-based algorithm for handling the problem that affects the performance of a machine learning classifier. The rules were crafted based on observing many cases of negation, simple linguistic knowledge, and sentiment lexicon. They also examine the impact of the proposed algorithm on the performance of different machine learning algorithms. Furthermore, they compare the performance of the classifiers when their algorithm is used against three baselines. The experimental results show that there is a positive impact on the classifiers when the proposed algorithm is used compared to the baselines.

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Section: Related Workmentioning

confidence: 99%

Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

Al-Harbi

2020

International Journal of Operations Research and Information Systems

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“…On Twitter, online motorcycle taxi companies have official accounts to inform services and receive tweet comments from the public and customers. Text polarity classification is done on documents, sentences [8,9]. Sentiment analysis is tasked with classifying polarity, meaning check whether the text in a document, sentence, or opinion contains positive or negative aspects [10].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM

Efendi

Sihombing²

2022

matrik

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The use of online food ordering through food systems or applications continues to increase, requiring vendors to implement marketing and sales strategies through surveys, feedback. The problems that arise are building a system analysis model from a collection of tweets with hashtags or usernames for ordering food online . The Support Vector Machine (SVM) algorithm is used for text classification. Tweets are collected into data sets, training data, and testing data, then a classification model of the SVM Algorithm is built. Preprocessing data, tweets are cleansing, tokenized, and stopword remove. From the collected tweets, they are grouped into 10 variables to identify demographic profiles. The results of the analysis are classified as positive sentiments, namely residence, price range, using promos, paid types, halal food while negative sentiments are ethnicity, culture, vegetarianism, place. Classification accuracy is important to validate the results of the SVM model. From 500 train data tweet, the resulting classification is 66% positive sentiment and 34% negative sentiment. Overall accuracy model Linier SVM result 83.2% with accuracy 92.55%.

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“…If the traditional method of negation handling using the 'NEG_' tag for words is used, irrelevant words get negated. To handle this, (Amalia et al, 2018) have proposed two methods for negation handling namely representing negation and word negated to determine the scope of negation. But this method is domain-specific and restricted to the Indonesian language.…”

Section: Introductionmentioning

confidence: 99%

SentiNeg

Savanur

Sumathi²

2022

International Journal of Software Innovation

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Sentiment analysis is the process of identifying and categorizing opinions computationally to determine the attitude expressed in the spoken or written text as positive, negative, or neutral. Negation analysis is the task of analyzing the negative opinions by identifying the scope of negation within a sentence and applying linguistic or grammatical rules of the language. In this paper, the rules for identifying the scope of negation within a sentence and the rules applicable to different negation categories are defined. An algorithm by the name SentiNeg has been proposed for processing negations at the sentence level. SentiNeg algorithm filters non-opinionated sentences from the data to avoid unnecessary processing. For opiniated sentences, the algorithm applies different linguistic or grammatical rules of the language to identify negative opinions. SentiNeg algorithm takes opinionated sentences as input and provides a detailed aspect-based summary of negative opinions that are expressed on the entity under analysis.

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Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter

Cited by 19 publications

References 11 publications

Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM

SentiNeg

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