A Survey on Sentiment Analysis

Subramanian, R. Raja; Akshith, Nukala; Murthy, Gogula Narasimha; Vikas, Manchala; Amara, Srikar; Balaji, Kamam

doi:10.1109/confluence51648.2021.9377136

Cited by 24 publications

(8 citation statements)

References 6 publications

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“…The methodology adopted in this study provides a rigorous approach for predicting sentiment and ratings of tourism user-generated content using ML and DL models ] and [Subramanian 2021]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Data were preprocessed by removing irrelevant information, duplicated reviews, and applying text normalization-finally, 24,228 valid records out of an initial dataset of 28,295. Two models have been developed in the model selection and training: ML and DL models, using five ML algorithms MNB, KNN, DT, GBC, XGBoost and a DL algorithm called ANN [Subramanian 2021].…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, e-commerce has revolutionized the retail industry, creating an abundance of UGC that can offer valuable insights into customer behavior and preferences [Duan 2022]. UGC comprises a range of customergenerated content, including reviews, feedback, and ratings, that can help businesses understand customer sentiments and improve the overall customer experience [Subramanian 2021]. ML and DL techniques have been widely used to analyze UGC, with studies demonstrating their effectiveness in predicting customer sentiment and rating from reviews ].…”

Section: Introductionmentioning

confidence: 99%

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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From Smart City to Smart Factory for Sustainable Future: conceptual framework, scenarios, and multidiscipline perspectivec (SCFF23)

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“…Sentiment analysis uses AI to identify emotional tones and sentiments in textual data. Machine learning algorithms are taught to identify linguistic patterns that indicate emotions like anger, fear, joy, sadness, and others using large labeled datasets [14]. These models can automatically classify patient messages as positive or negative.…”

Section: Leveraging Ai Sentiment Analysis To Understand and Predict P...mentioning

confidence: 99%

Psychological AI: A critical analysis of capabilities, limitations, and ramifications

Wang

2024

ACE

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As artificial intelligence (AI) continues to advance, machine learning and natural language processing (NLP) offer new possibilities in various areas of psychology, such as therapy, diagnostics, treatment planning, demographic profiling, sentiment analysis, and consumer psychology. However, AI research in psychology is still lacking. This study aims to explore the specific applications of AI in the field of psychology. Firstly, the article delves into the efficacy and cost-effectiveness of chatbots in delivering cognitive behavioral therapy (CBT). Next, the article explores the current state of AI in detecting mental health disorders, highlighting the use of digital footprints as a diagnostic tool. Furthermore, the research investigates how AI can assist medical professionals in developing focused treatment plans. It analyzes studies that aim to optimize treatment strategies using AI technologies. The article also examines the influence of demographic characteristics on our understanding and application of psychology. Lastly, the article touches on the use of AI in analyzing sentiment to understand and predict consumer behavior. Ethical implications of using AI-driven psychological assessments are also discussed. It concludes with a summary of key findings and offers a glimpse into potential future developments in this field.

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“…A fast-growing and improving sub-field of Natural Language Processing (NLP) is sentiment analysis. Subramaniam et al conducted a sentiment analysis-based survey in their research [4]. They studied the IMDB dataset and observed that sequence neural models had the best results, but they come at a high cost in terms of classifier complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Samia¹,

Rajee²,

Hasan³

et al. 2022

IJACSA

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Public opinion is important for decision-making on numerous occasions for national growth in democratic countries like Bangladesh, the USA, and India. Sentiment analysis is a technique used to determine the polarity of opinions expressed in a text. The more complex stage of sentiment analysis is known as Aspect-Based Sentiment Analysis (ABSA), where it is possible to ascertain both the actual topics being discussed by the speakers as well as the polarity of each opinion. Nowadays, people leave comments on a variety of websites, including social networking sites, online news sources, and even YouTube video comment sections, on a wide range of topics. ABSA can play a significant role in utilizing these comments for a variety of objectives, including academic, commercial, and socioeconomic development. In English and many other popular European languages, there are many datasets for ABSA, but the Bengali language has very few of them. As a result, ABSA research on Bengali is relatively rare. In this paper, we present a Bengali dataset that has been manually annotated with five aspects and their corresponding sentiment. A baseline evaluation was also carried out using the Bidirectional Encoder Representations from Transformers (BERT) model, with 97% aspect detection accuracy and 77% sentiment classification accuracy. For aspect detection, the F1-score was 0.97 and for sentiment classification, it was 0.77.

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A Survey on Sentiment Analysis

Cited by 24 publications

References 6 publications

From Smart City to Smart Factory for Sustainable Future: conceptual framework, scenarios, and multidiscipline perspectivec (SCFF23)

From Smart City to Smart Factory for Sustainable Future: conceptual framework, scenarios, and multidiscipline perspectivec (SCFF23)

Psychological AI: A critical analysis of capabilities, limitations, and ramifications

Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

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