Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Zou, Haochen; Xiang, Kun

doi:10.3390/e24030398

Cited by 12 publications

(6 citation statements)

References 54 publications

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“…In this modern era, in intelligent analyzing of data and developing the related smart applications, the understanding of IoT [15]- [17], cyber-security [18], in particular, machine learning and deep learning [19]- [25] are crucial. According to our requirement, we update our model and dataset using machine learning approach.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Sentiment analysis on Bangla conversation using machine learning approach

Hassan

Shakil²,

Moon³

et al. 2022

IJECE

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<span>Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.</span>

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Section: Experimental Results and Analysismentioning

confidence: 99%

Sentiment analysis on Bangla conversation using machine learning approach

Hassan

Shakil²,

Moon³

et al. 2022

IJECE

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“…For example, Kydros et al analyzed tweets from Twitter during the time of the coronavirus outbreak that helped government agencies develop effective strategies by understanding people's emotions [38]. Zou et al designed an emotion analysis method that combines emojis and other emoticons with short texts to capture the deep value of public opinion and help companies optimize their services [40].…”

Section: Related Workmentioning

confidence: 99%

Lexicon-Enhanced Multi-Task Convolutional Neural Network for Emotion Distribution Learning

Dong

Zeng

2022

Axioms

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Emotion distribution learning (EDL) handles emotion fuzziness by means of the emotion distribution, which is an emotion vector that quantitatively represents a set of emotion categories with their intensity of a given instance. Despite successful applications of EDL in many practical emotion analysis tasks, existing EDL methods have seldom considered the linguistic prior knowledge of affective words specific to the text mining task. To address the problem, this paper proposes a text emotion distribution learning model based on a lexicon-enhanced multi-task convolutional neural network (LMT-CNN) to jointly solve the tasks of text emotion distribution prediction and emotion label classification. The LMT-CNN model designs an end-to-end multi-module deep neural network to utilize both semantic information and linguistic knowledge. Specifically, the architecture of the LMT-CNN model consists of a semantic information module, an emotion knowledge module based on affective words, and a multi-task prediction module to predict emotion distributions and labels. Extensive comparative experiments on nine commonly used emotional text datasets showed that the proposed LMT-CNN model is superior to the compared EDL methods for both emotion distribution prediction and emotion recognition tasks.

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“…They are used to convey ideas or feedback that cannot be easily written in words . Emojis are highly correlated with the sentiment orientation of short texts (Zou & Xiang, 2022). In sentiment analysis, emojis can be processed during preprocessing by converting them into text (Azzahra & Wibowo, 2020).…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-Nearest Neighbor (KNN) and Logistic Regression Algorithms in Sentiment Analysis Evermos App Reviews

Hasana,

Munggaran

2024

Eduvest

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The Evermos application is one of the e-commerce applications for resellers and dropshippers in Indonesia. To find out the quality of the application requires an assessment from application users. One method that can be used is sentiment analysis. Sentiment analysis is one of the text mining techniques that can help in processing a judgment, complaint, perception or response to a particular object. In this study, the sentiment analysis carried out was the analysis of Evermos application review text on the Google Play Store accompanied by emoji conversion by comparing the K-Nearest Neighbor (KNN) model and Logistic Regression. The comparison of these two classification models was done to get the best accuracy in the sentiment analysis. The results of the two test models that have been carried out, the Logistic Regression model is a classification model that has the best accuracy results with a value of 98.1% in test data and 82.7% in training data.

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Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Cited by 12 publications

References 54 publications

Sentiment analysis on Bangla conversation using machine learning approach

Sentiment analysis on Bangla conversation using machine learning approach

Lexicon-Enhanced Multi-Task Convolutional Neural Network for Emotion Distribution Learning

Implementation of K-Nearest Neighbor (KNN) and Logistic Regression Algorithms in Sentiment Analysis Evermos App Reviews

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