Performance Evaluation of Machine Learning and Deep Learning Techniques for Sentiment Analysis

Mehta, Anushka; Parekh, Yash; Karamchandani, Sunil

doi:10.1007/978-981-10-7512-4_46

Cited by 6 publications

(15 citation statements)

References 12 publications

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“…In the first step, n-gram frequencies were generated. N-gram refers to a contiguous sequence of n words from a given sequence of text Mehta, Parekh, & Karamchandani, 2018). Next, the 'Bagof-Words' (BoW) technique was employed De Vries, Schoonvelde, & Schumacher, 2018;, which is based on the concept of representation of documents as a collection of words, regardless of grammar and order.…”

Section: Automated Text Classificationmentioning

confidence: 99%

Potential for Blocking Advancement: Teaching Surveys for Student Evaluation of Lecturers

Eckhaus¹,

Davidovitch²

2019

INT J EDUC METHODOL

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In the current study we examined the relationships between student evaluations of lecturers (teaching surveys) and faculty members' perceptions of these surveys as capable of blocking and limiting their professional advancement. Faculty members are judged and evaluated by academic authorities for their academic performance in research and teaching. 178 questionnaires were collected from the faculty of several academic institutions. We employ a mix method analysis, and form a model that reflects the factors perceived by faculty members as having the potential to block their professional advancement in academia. The research findings show that lecturers are of the opinion that teaching load has a detrimental effect on students' evaluations in the surveys. Lecturers at the beginning of their academic life, those in lower ranks: senior teacher and senior lecturer, address the negative aspects of the surveys more than others. The research findings indicate that although more hours are taught in colleges than at universities, it is harder to receive positive survey ratings at colleges. Moreover, since in Israeli academia research is still the main criterion for promotion-faculty members born in Israel were found to teaching less than those born elsewhere. Hence, faculty members think that student surveys are destructive and entail risks for their professional advancement. Assuming that students' voice and opinions on teaching are important-how can a balance be achieved between the research achievements of faculty members and student satisfaction?

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Section: Automated Text Classificationmentioning

confidence: 99%

Potential for Blocking Advancement: Teaching Surveys for Student Evaluation of Lecturers

Eckhaus¹,

Davidovitch²

2019

INT J EDUC METHODOL

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“…Nowadays, sentiment analysis includes more than reviewing products and services. It has expanded to include dealing with politics and technology [24]. Generally, the sentiment is classified as negative, positive, and sometimes neutral opinions as presented in the dataset on which the classifier model will be trained [1], [17].…”

Section: Background Informationmentioning

confidence: 99%

“…1) Traditional techniques: which are used with classical machine learning algorithms [24] such as Support Vector Machine (SVM), Linear regression (LR), and Naïve Bayes (NB). These techniques are:…”

Section: B Feature Extraction Techniquesmentioning

confidence: 99%

“…(2) N-grams: refers to a contiguous sequence of number of (n) words. This technique could be applied to achieve more efficient classification by capturing the most frequent n-grams instead of using the entire corpora [24]. n-grams technique was used for sarcasm detection in Arabic with different combinations of classical algorithms in [47].…”

Section: B Feature Extraction Techniquesmentioning

confidence: 99%

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A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends

2023

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On social media platforms, it is essential to express one's thoughts, opinions, and reviews. One of the most widely used linguistic forms to criticize or express a person's ideas with ridicule is sarcasm, where the written text has both intended and unintended meanings. The sarcastic text frequently reverses the polarity of the sentiment. Therefore, detecting sarcasm in the text has a positive impact on the sentiment analysis task and ensures more accurate results. Although Arabic is one of the most frequently used languages for web content sharing, the sarcasm detection of Arabic content is restricted and yet still naive due to several challenges, including the morphological structure of the Arabic language, the variety of dialects, and the lack of adequate data sources. Despite that, researchers started investigating this area by introducing the first Arabic dataset and experiment for irony detection in 2017. Thus, our review focuses on studies published between 2017 and 2022 on Arabic sarcasm detection. We provide a thorough literature review of Artificial Intelligence (AI) techniques and benchmarks used for Arabic sarcasm detection. In addition, the challenges of Arabic sarcasm detection are investigated, along with future directions, focusing on the challenge of publicly available Arabic sarcasm datasets.INDEX TERMS Artificial intelligence (AI), Arabic sarcasm detection, deep learning (DL), machine learning (ML), natural language processing (NLP), sentiment analysis (SA).

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“…Thus, a literature analysis on a Twitter debate topic may help machine and deep learning-based opinion categorization. Deep-learning-based methods automatically pick and learn features from the textual information, unlike machine learningbased methods [6]. Many degrees of deep learning view data differently.…”

Section: Introductionmentioning

confidence: 99%

Aspect based Sentiment & Emotion Analysis with ROBERTa, LSTM

Sirisha¹,

Chandana²

2022

IJACSA

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Internet usage has increased social media over the past few years, significantly impacting public opinion on online social networks. Nowadays, these websites are considered the most appropriate place to express feelings and opinions. The popular social media site Twitter offers valuable insight into people's thoughts. Throughout the conflict between Russia and Ukraine, people from all over the world have expressed their opinions. In this study, "machine-learning" and "deep-learning" techniques are used to understand people's emotions and their views about this war are revealed. This study unveils a novel deeplearning approach that merges the best features of the sequence and transformer models while fixing their respective flaws. The model combines Roberta with ABSA (Aspect based sentiment analysis) and Long Short-Term Memory for sentiment analysis. A large dataset of geographically tagged tweets related to the Ukraine-Russia war was collected from Twitter. We analyzed this dataset using the Roberta-based sentiment model. In contrast, the Long Short-Term Memory model can effectively capture longdistance contextual semantics. The Robustly optimized BERT with ABSA approach maps words into a compact, meaningful word embedding space. The accuracy of the suggested hybrid model is 94.7%, which is higher than the accuracy of the stateof-the-art techniques.

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Performance Evaluation of Machine Learning and Deep Learning Techniques for Sentiment Analysis

Cited by 6 publications

References 12 publications

Potential for Blocking Advancement: Teaching Surveys for Student Evaluation of Lecturers

Potential for Blocking Advancement: Teaching Surveys for Student Evaluation of Lecturers

A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends

Aspect based Sentiment & Emotion Analysis with ROBERTa, LSTM

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