Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi, Monali; Biswas, Saroj Kumar

doi:10.1007/s10462-023-10442-2

Cited by 59 publications

(22 citation statements)

References 243 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its calculation is based on a combination of two metrics, one of which measures how many times a word appears in a collection of documents, and the other measures the word’s inverse document frequency. In a document, term frequency (TF) counts the number of times words appear, and inverse document frequency (IDF) is a method that helps distinguish and classify documents easily by giving importance or weightage to words that are unique to a certain set of documents [ 30 ]. Words in the document with high or low-frequency terms are given more weight by the IDF.…”

Section: Proposed Systemmentioning

confidence: 99%

Enhancing machine learning-based sentiment analysis through feature extraction techniques

A. Semary,

Ahmed,

Amin

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model’s performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

show abstract

Section: Proposed Systemmentioning

confidence: 99%

Enhancing machine learning-based sentiment analysis through feature extraction techniques

A. Semary,

Ahmed,

Amin

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The significant works done for aspect term extraction and aspect-based sentiment analysis in general and in the Hindi language are discussed in the literature survey (3)(4)(5)(6)(7)(8) . Md Shad Akhtar et al (9) reported the first supervised approach to the aspect term extraction task; they created an annotated dataset of high quality and built a machine learning model for sentiment analysis to show practical usage of the dataset.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Gupta,

Sharma

2024

IJST

View full text Add to dashboard Cite

Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field. Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi

show abstract

“…Sentiment analysis In product design, sentiment analysis plays a crucial role in identifying customers' emotional attitudes towards a product and classifying them as positive, negative, or neutral. Sentiment analysis can be conducted at three different levels [141][142][143] : (i) document-level, which is a coarse-grained analysis of all reviews in a document; (ii) sentence-level, which is a medium-grained analysis of individual sentences; and (iii) aspect (attribute)-level, which is a fine-grained analysis of specific product attributes.…”

Section: Data Processingmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

View full text Add to dashboard Cite

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI driven product design methodology with significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review on big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited in aiding product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

show abstract

Sentiment analysis: A survey on design framework, applications and future scopes

Cited by 59 publications

References 243 publications

Enhancing machine learning-based sentiment analysis through feature extraction techniques

Enhancing machine learning-based sentiment analysis through feature extraction techniques

Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Big Data and AI-driven Product Design: A Survey

Contact Info

Product

Resources

About