Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

Habib, Mohammad W.; Sultani, Zainab N.

doi:10.22401/anjs.24.3.08

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…The dataset is partitioned into training and testing label columns to evaluate the effectiveness of various machine-learning strategies. According to the hypothesis presented in [15], feature extraction methodologies are specified in conjunction with approaches to SA. An analysis of feature extraction strategies is presented here, using a wide variety of dataset domains.…”

Section: Related Workmentioning

confidence: 99%

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Hicham

Karim

Habbat

2023

IJECE

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<span lang="EN-US">Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).</span><p> </p>

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Section: Related Workmentioning

confidence: 99%

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Hicham

Karim

Habbat

2023

IJECE

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“…To address this constraint, they proposed that future research use larger and more varied datasets to improve performance and eliminate biases. M. W. Habib and Z. N. Sultani [34] (2021), utilized a machine learning strategy to identify tweets in the Sentiment140 dataset as having positive or negative sentiment. To reduce and extract features from unstructured Twitter text data, the author examines three models, Naive Bayes, Logistic Regression, and Support Vector Machine, and employs four distinct feature extraction approaches, including BOW, TF-IDF, doc2vec, and word2vec.…”

Section: Implementation Of Feature Extraction and Algorithmsmentioning

confidence: 99%

Systematic review of sentiment analysis and predict sarcastic

Kzar,

Safi

2023

J. Al-Qadisiyah Comp. Sci. Math.

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With the rise of social media on the web, sentiment analysis has become one of the most important areas of study. Today, millions of people share their thoughts, ideas, feelings, and opinions on social media sites like Twitter and Facebook. Sentiment analysis, also called opinion mining, is mostly about classifying and predicting how people feel about a certain target. It involves putting text documents or sentences into groups based on how positive or negative they are about a certain topic. Researchers always find "natural language processing" to be one of the most interesting topics. To solve different problems and improve the accuracy of different applications, it is always helpful to know the exact meaning of what is being said in a conversation. Sentiment analysis uses natural language processing (NLP) and learning models like machine learning and deep learning algorithms to figure out how people feel about the data given. Sentiment analysis looks at sarcasm because sarcasm is a way for people to say how they feel about something without saying it directly. People means the exact opposite of what the sentence says at first glance. Sarcasm is hard to figure out because each sarcastic sentence is different. This paper will talk about what has been done in the field of sarcasm detection, the different techniques used, and the problems that still need to be solved.

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“…The internal implementation of Fasttext discards the word order information, depending on the score matrix. It sums up a score when one or more words with a high absolute value significantly influence the final decision [2,16].…”

Section: Feature Extraction and Word Embeddingmentioning

confidence: 99%

“…In recent years, the number of users of social media sites such as Facebook, Twitter, Instagram, and others has increased, where any user can share their opinions on any topic on social media sites [1,2]. This frequent use of social media sites can cause many problems, especially for teenagers and children aged 13 to 22 years [3].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Cyberbullying Detection in Social Media for the Last Five Years

Haydar,

N. Dhannoon

2023

ANJS

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The number of users of social media sites has increased nowadays, and while these sites have many benefits, they also have many damages that have grown with the increasing number of users. Among these damages that have spread in social media sites in our time is the phenomenon of cyberbullying. It has become necessary to find solutions to detect it to prevent and hold bullies accountable to reduce the phenomenon of cyberbullying, which has great health and mental effects on the victim in society. There have been many attempts to build models to detect and classify cyberbullying by using machine learning and deep learning algorithms with different sets of data that were collected from social media sites such as Twitter, YouTube, Facebook, Instagram, and others. In this work, we show a group of previous studies that used machine learning and deep learning algorithms in good attempts to detect and classify the phenomenon of cyberbullying.

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Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

Cited by 9 publications

References 14 publications

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Systematic review of sentiment analysis and predict sarcastic

A Comparative Study of Cyberbullying Detection in Social Media for the Last Five Years

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