E-Commerce Product Reviews Using Aspect Based Hindi Sentiment Analysis

Yadav, Vikas; Verma, Parul; Katiyar, Vinodini

doi:10.1109/iccci50826.2021.9402365

Cited by 13 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of sentiment analysis, machine learning has proven to be an effective approach for determining which side of an argument has been taken. Sentiment analysis relies on pre-processing, labeling, categorization, and tokenization at the sentence level of text [20].…”

Section: Proposed Workmentioning

confidence: 99%

Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews

Sharma,

Dagur

2023

IJRITCC

View full text Add to dashboard Cite

To determine an author's emotional state from their written words is the focus of sentiment analysis, a subfield of NLP. This study focuses on the many techniques used to categorize the text reviews written in natural language according to the viewpoints expressed therein, in order to determine if the widespread behavior is positive, negative, or neutral. Streaming of thoughts and expression of opinion have been facilitated by the proliferation of debate forums, Weblogs, product review sites, e-commerce, and social networking sites. A lot of people's feelings, reviews, and assessments of others' opinions can be found on social media. This research ranks the top classifier for feelings using data derived from online product reviews posted to Twitter. Experimental work on polarity classification with well-known classifiers such as Naive byes, Support vector machine, and Logistic regression for anticipating testimonials was addressed.

show abstract

Section: Proposed Workmentioning

confidence: 99%

Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews

Sharma,

Dagur

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…To offer conclusive suggestions, the area of aspect-based sentiment analysis (ABSA) analyses and classifies the views stated on the many aspects included in these opinions. To broaden an interpretation of the Hindi text submitted, this essay examines the evolution of the ABSA model in Hindi reviews [11].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Consumer Reviews Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

Internet and social media platforms such as Twitter, Facebook, and several blogs provide various types of helpful information worldwide. The increased usage of social media and e-commerce websites is constantly generating a massive volume of data about image/video, sound, text, etc. The text among these is the most significant type of unstructured data, requiring special attention from researchers to acquire meaningful information. Recently, many techniques have been proposed to obtain insights from these data. However, there are still challenges in dealing with the text of enormous size; therefore, accurate polarity detection of consumer reviews is an ongoing and exciting problem. Due to this, it is challenging to derive exact meanings from the textual data from consumer reviews, comments, tweets, posts, etc. Previously, a reasonable amount of work has been conducted to simplify the extraction of exact meanings from these data. A unique technique that includes data gathering, preprocessing, feature encoding, and classification utilizing three long short-term memory variations is presented to address sentiment analysis problems. Analysing appropriate data collection, preprocessing, and classification is crucial when interpreting such data. Different textual datasets were used in the studies to gauge the importance of the suggested models. The proposed technique of predicting sentiments shows better, or at least comparable, results with less computational complexity. The outcome of this work shows the significant importance of sentiment analysis of consumer reviews and social media content to obtain meaningful insights.

show abstract

Aspect sentiment quadruple extraction based on the sentence-guided grid tagging scheme

Zhu

Bao

et al. 2023

World Wide Web

View full text Add to dashboard Cite

E-Commerce Product Reviews Using Aspect Based Hindi Sentiment Analysis

Cited by 13 publications

References 1 publication

Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews

Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews

Sentiment Analysis of Consumer Reviews Using Deep Learning

Aspect sentiment quadruple extraction based on the sentence-guided grid tagging scheme

Contact Info

Product

Resources

About