A Study of Sentiment Analysis: Concepts, Techniques, and Challenges

Aqlan, Ameen Abdullah Qaid; Manjula, B.; Naik, R. Lakshman

doi:10.1007/978-981-13-6459-4_16

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…The three main methods for identifying and classifying emotions in text are lexicographical, ML and hybrid techniques. Aqlan et al, stated that the ML approach consider texts as a categorization problem that may be further divided into three categories: semisupervised, supervised, and unsupervised learning [16]. On the other hand, linguistic polarity is utilized in the lexicon-based method.…”

Section: Sentiment Analysis Approachesmentioning

confidence: 99%

Classification of cyberbullying in social media using Natural Language Programming method

Vivekananth,

Sharma

2024

Preprint

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The evolution of internet describes the usage of Social Media (SM) which expands drastically. This involvement continues to increase in context with the current global epidemic since people frequently use SM platforms to vent their feelings. Similarly, the widespread adoption of SM sites like Facebook and Twitter by many organizations has raised the volume of essential individual input regarding the state of affairs, events, goods, and services. One of the major drawbacks that widespread SM usage is cyberbullying. The frequency of cyberbullying on SM platforms has raised serious concerns for people, organizations, and society at large. To minimize negative impacts due to cyberbullying on SM, early detection is essential. As a result, Sentiment Analysis (SA) utilizing Twitter data has gained prominence. Text analysis-related Natural Language Processing (NLP) and in Artificial Intelligence (AI) technologies have gained increased attention because of increasing demand for SM analysis. Moreover, the important fields utilized to proactively extract and train high-quality characteristics from low-level text involved is Machine Learning (ML). The Convolution Neural Network (CNN) models are Deep Learning (DL) methods to train data like text, picture, and video data. Improved text classification is achieved by preparing these data types using CNN as a persuasive method. To categorize the bullying text, this study discusses the Ensemble model that integrates Modified Term Frequency and Inverse Document Frequency (MTF-IDF), and Deep Neural Network (DNN) with sophisticated feature extraction approaches. From the text data, the cyberbullying feature patterns are extracted using the techniques of feature extraction.

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Section: Sentiment Analysis Approachesmentioning

confidence: 99%

Classification of cyberbullying in social media using Natural Language Programming method

Vivekananth,

Sharma

2024

Preprint

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“…Some of the most popular types of SA are: fine-grained SA, emotion detection, aspect-based SA and multilingual SA ( 23 ). Fine-grained SA considers an expanded number of polarity categories (e.g., very positive / positive / neutral / negative / very negative).…”

Section: Medical Sentiment Analysis—introductory Conceptsmentioning

confidence: 99%

“…Emotion detection uses lexicons or ML/NLP systems to detect sentiments. Aspect-based SA highlights which particular aspects or features people are mentioning in a positive, neutral, or negative way ( 23 ). Multilingual SA techniques have been developed in order to analyses data in different languages ( 24 ).…”

Section: Medical Sentiment Analysis—introductory Conceptsmentioning

confidence: 99%

“Not by Our Feeling, But by Other's Seeing”: Sentiment Analysis Technique in Cardiology—An Exploratory Review

Brezulianu

Burlacu

Popa

et al. 2022

Front. Public Health

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Sentiment Analysis (SA) is a novel branch of Natural Language Processing (NLP) that measures emotions or attitudes behind a written text. First applications of SA in healthcare were the detection of disease-related emotional polarities in social media. Now it is possible to extract more complex attitudes (rank attitudes from 1 to 5, assign appraisal values, apply multiple text classifiers) or feelings through NLP techniques, with clear benefits in cardiology; as emotions were proved to be veritable risk factors for the development of cardiovascular diseases (CVD). Our narrative review aimed to summarize the current directions of SA in cardiology and raise the awareness of cardiologists about the potentiality of this novel domain. This paper introduces the readers to basic concepts surrounding medical SA and the need for SA in cardiovascular healthcare. Our synthesis of the current literature proved SA's clinical potential in CVD. However, many other clinical utilities, such as the assessment of emotional consequences of illness, patient-physician relationship, physician intuitions in CVD are not yet explored. These issues constitute future research directions, along with proposing detailed regulations, popularizing health social media among elders, developing insightful definitions of emotional polarity, and investing research into the development of powerful SA algorithms.

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“…According to Aqlan et al (2019), with the huge explosion in information in recent years in the sites of communication, air traffic, and alternative markets, all this huge amount of information cannot be controlled and analyzed using the traditional way, so the scientists and researchers developed a high-efficiency technique to deal with this data. This requires sentiment analysis to process data and know its polarity to determine the right decision.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on User Reviews of Mutual Fund Applications

Madyatmadja¹,

Shinta²,

Susanti³

et al. 2022

Journal of Computer Science

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The primary goal of this study is to compare the accuracy of the results of sentiment analysis using the Naive Bayes, Support Vector Machine (SVM), and Random Forest methods on one of the mutual fund application's user reviews. The second goal is to identify user reviews of the mutual fund app to gain insight into the topics covered by each sentiment. The user reviews have been collected through a web scraping method on the google play store, then cleaned through several processes of data pre-processing. Feature extraction was performed using TF-IDF along with vectorization using ngrams. The model performance was measured using a confusion matrix. Using a ratio of 80:20 on training and testing data, resulting in an accuracy of 92.7, 93.7 and 94.2% for Naive Bayes, SVM, and Random Forest methods, respectively. Identify the topics covered by each sentiment in user reviews using visualizations. In the positive sentiment of users, the majority discusses the application which is easy and good, especially for novice investors. In negative sentiment, the majority discussed the slow sales process to disbursement of funds and long loading times when opening the application.

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A Study of Sentiment Analysis: Concepts, Techniques, and Challenges

Cited by 20 publications

References 25 publications

Classification of cyberbullying in social media using Natural Language Programming method

Classification of cyberbullying in social media using Natural Language Programming method

“Not by Our Feeling, But by Other's Seeing”: Sentiment Analysis Technique in Cardiology—An Exploratory Review

Sentiment Analysis on User Reviews of Mutual Fund Applications

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