Product’s behaviour recommendations using free text: an aspect based sentiment analysis approach

Nawaz, Asif; Awan, Ashfaq Ahmed; Ali, Tariq; Rana, Muhammad Rizwan Rashid

doi:10.1007/s10586-019-02995-1

Cited by 15 publications

(6 citation statements)

References 19 publications

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“…TF/IDF and POS tagging techniques were used to extract features. In [36] recommendation of behaviors with the help of aspect level sentiment analysis. The dataset that was used consists of 2590 hotel and cars reviews and 3700 mobile reviews (Galaxy S8).…”

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

View full text Add to dashboard Cite

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“…Experimental results show that the proposed model achieved 91.0% precision, 91.1% recall and 91.0% F-measure. Nawaz et al presented an aspect-based sentiment analysis technique using a POS tagger, Visuwords and a rational classifier [26]. Dey et al presented a work with a better rate of accuracy and faster recognition time for monitoring the emotional state of humans [27].…”

Section: Mowlaei Et Al Developed Extensions Of Two Lexicon Generation...mentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana¹,

Rehman²,

Nawaz³

et al. 2023

Computer Systems Science and Engineering

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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

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“…Data preprocessing is an important step in preparing text for classification, particularly in online contexts where noise and irrelevant content can be prevalent. This noise may include HTML tags, advertisements, and other distracting elements [23]. Moreover, words within the text often have minimal impact on the classification task but still contribute to the overall complexity because each word is treated as a distinct dimension.…”

Section: A Data Preprocessingmentioning

confidence: 99%

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

Rana,

Nawaz,

Ali

et al. 2023

Eng. Technol. Appl. Sci. Res.

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The advancement of technology has led to the rise of social media forums and e-commerce platforms, which have become popular means of communication, and people can express their opinions through comments and reviews. Increased accessibility to online feedback helps individuals make informed decisions about product purchases, services, and other decisions. This study used a sentiment analysis-based approach to improve the functionality of the recommendations from user reviews and consider the features (aspects and opinions) of products and services to understand the characteristics and attributes that influence the performance of classification algorithms. The proposed model consists of data preprocessing, word embedding, character representation creation, feature extraction using BiLSTM-CF, and classification using BiGRU. The proposed model was evaluated on different multidomain benchmark datasets demonstrating impressive performance. The proposed model outperformed existing models, offering more promising performance results in recommendations.

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Product’s behaviour recommendations using free text: an aspect based sentiment analysis approach

Cited by 15 publications

References 19 publications

Aspect Level Songs Rating Based Upon Reviews in English

Aspect Level Songs Rating Based Upon Reviews in English

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

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