Deep sequential model for review rating prediction

Verma, Sharad; Saini, Mayank; Sharan, Aditi

doi:10.1109/ic3.2017.8284318

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…In the paper [10], have used a deep sequential model for review rating prediction deals with amazon's electronic dataset rating prediction. Deep learning mechanisms such as LSTM, GRNN and a combination of LSTM and GRNN is proposed and the combination gives higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa

U.C.

N.M.

2022

IJRITCC

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In this era of cut-throat competition among traditional and newer digital organizations, capturing, listening, and understanding customer voices are critical for success in the marketplace. The challenge to decipher the voice of the customer (VOC) has multiplied many times today, as now the number of customer reviews are present in multiple platforms and the data to be analyzed is huge. Sentiment analysis, and text analytics using machine learning, deep learning tools and transformer-based tools can be applied to gather meaningful insights from these data. This paper applies the traditional machine learning tools of the Naive Bayes classifier, Random Forest and AdaBoost to predict the customer review ratings. These results are compared with deep learning methods of CNN, RNN and Bi-LSTM and transformer-based approaches of BERT, DistilBERT and RoBERTa. The results show that RoBERTa has the highest accuracy among these methods. Paper also uses the explainable AI tool of LIME to understand the customer sentiments deeper in terms of why customers are giving a particular rating to the product. Business organizations will continue to use more and more AI tools to understand the customer feedback and the attempt in this paper is to learn how we can make predictions faster and more accurately.

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Section: Related Workmentioning

confidence: 99%

Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa

U.C.

N.M.

2022

IJRITCC

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“…No usage of advanced NLP techniques was found. One major drawback in analyzing the sentiments of reviews, lies in the technique of modeling long term dependencies which are so complicatedly found in the short text reviews [19]. A deep sequential model is built for sentiment analysis using Gated RNN for dependency between sentences and LSTM to vectorize the sentences.…”

Section: Literature Reviewmentioning

confidence: 99%

A Conceptual Data Modelling Framework for Context-Aware Text Classification

Tazeen¹,

Sandhya²

2020

IJACSA

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Data analytics has an interesting variant that aims to understand an entity's behavior. It is termed as diagnostic analytics, which answers "why type questions". "Why type questions" find their applications in emotion classification, brand analysis, drug review modeling, customer complaints classification etc. Labeled data form the core of any analytics' problem, leave alone diagnostic analytics; however, labeled data is not always available. In some cases, it is required to assign labels to unknown entities and understand its behavior. For such scenarios, the proposed model unites topic modeling and text classification techniques. This combined data model will help to solve diagnostic issues and obtain meaningful insights from data by treating the procedure as a classification problem. The proposed model uses Improved Latent Drichlet Allocation for topic modeling and sentiment analysis to understand an entity's behavior and represent it as an Improved Multinomial Naïve Bayesian data model to achieve automated classification. The model is tested using drug review dataset obtained from UCI repository. The health conditions with their associated drug names were extracted from the reviews and sentiment scores were assigned. The sentiment scores reflected the behavior of various drugs for a particular health condition and classified them according to their quality. The proposed model performance is compared with existing baseline models and it is proved that our model exhibited better than other models.

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