Aspect Based Sentiment Analysis for Online Reviews

Xu, Lamei; Liu, Jin; Wang, Lina; Yin, Chunyong

doi:10.1007/978-981-10-7605-3_78

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…SVM performed 72.4% in terms of accuracy. In [42] sentiment analysis of online reviews was performed on the Yelp dataset at aspect level, (i) Laptop reviews and (ii) restaurant reviews. A total 4,934 number of reviews were collected.…”

Section: Literature Reviewmentioning

confidence: 99%

Aspect Level Songs Rating Based Upon Reviews in English

Qureshi¹,

Asif²,

Anwar³

et al. 2023

Computers, Materials &Amp; Continua

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With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms-Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.

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Section: Literature Reviewmentioning

confidence: 99%

Aspect Level Songs Rating Based Upon Reviews in English

Qureshi¹,

Asif²,

Anwar³

et al. 2023

Computers, Materials &Amp; Continua

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“…Although the research enriches ABSA domain with emotional affects, it could not cover the emotional classes such as joy, disgust, sadness etc in their analysis. In another effort to minimize the effect of weak semantic vector representation for SA in paragraph, sentences and phrases, [52] used CRF for the extraction of opinion target expressions. However, its drawback comes from the polarity identification approach, which lacks the capacity of handling the positive, negative, and neutral polarity categories.…”

Section: ) Explicit Aspect Extraction Techniques With Their Associatmentioning

confidence: 99%

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

et al. 2020

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Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain. INDEX TERMS aspect-based sentiment analysis, aspect extraction, explicit aspect, feature extraction, implicit aspect.

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“…Sentiment analysis is a popular data mining and NLP topic both in the industry and academia . The task of sentiment analysis can be a binary classification of positive and negative opinions, as well as extracting the strength of the sentiment…”

Section: Deep Learning Models For Sentiment Classificationmentioning

confidence: 99%

“…Sentiment analysis is a popular data mining and NLP topic both in the industry and academia. 33,34 The task of sentiment analysis can be a binary classification of positive and negative opinions, 35,36 as well as extracting the strength of the sentiment. 37,38 Wehrmann et al 39 proposed a language agnostic sentiment analysis model that can be built on text from different languages.…”

Section: Sentiment Analysismentioning

confidence: 99%

Evaluating deep learning models for sentiment classification

Karakus

Talo

Hallac

et al. 2018

Concurrency and Computation

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Summary Deep learning has emerged as an effective solution to various text mining problems such as document classification and clustering, document summarization, web mining, and sentiment analysis. In this paper, we describe our work on investigating several deep learning models for a binary sentiment classification problem. We used movie reviews in Turkish from the website http://www.beyazperde.com to train and test the deep learning models. We also report a detailed comparison of the models in terms of accuracy and time performances. Two major deep learning architectures used in this study are Convolutional Neural Networks and Long Short‐Term Memory. We built several variants of these models by changing the number of layers, tuning the hyper‐parameters, and combining models. Additionally, word embeddings were created by applying the word2vec algorithm with a skip‐gram model on a large dataset (∼ 13 M words) composed of movie reviews. We investigate the effect of using the pre‐word embeddings with these models. Experimental results have shown that the use of word embeddings with deep neural networks effectively yields performance improvements in terms of run time and accuracy.

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Aspect Based Sentiment Analysis for Online Reviews

Cited by 7 publications

References 7 publications

Aspect Level Songs Rating Based Upon Reviews in English

Aspect Level Songs Rating Based Upon Reviews in English

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

Evaluating deep learning models for sentiment classification

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