Paper Title:
Latent Aspect Detection from Online Unsolicited Customer Reviews
Introduction:
Aspect Detection helps businesses understand customer opinions and address potential issues, thereby preventing customer churn. Identifying and analyzing aspects in customer reviews allows businesses to prioritize improvements and make informed decisions to retain customers.
Main Problem:
Current methods for aspect detection often rely on supervised learning, which involves training on manually labeled data. However, this approach becomes impractical when dealing with online reviews that are typically shorter, more informal, and where aspects are often implicitly expressed rather than explicitly mentioned. This paper presents an unsupervised method to extract latent aspects that are common in online reviews but not overtly stated.
Illustrative Example:
Implicit aspect: "We were given a table far from the river." [Aspect: Management]
Explicit aspect: "The menu is extensive, and there’s a bar with live music." [Aspect: Bar/Menu]
Input:
A review from an online platform (e.g., restaurant reviews).
Output:
Detected latent aspects present in the review without explicit surface forms.
Motivation:
Previous works in this area have not effectively addressed the challenge of detecting latent aspects. Existing unsupervised methods have mainly focused on explicit aspects and neglected latent ones, making it necessary to develop an approach specifically targeting latent aspects.
Related Works and Their Gaps:
Rule-based methods (Hai et al., 2011; Zeng and Li, 2013) use association rule mining to detect hidden aspects but are limited in scalability across various domains.
Supervised methods (Peng et al., 2020; Zhang et al., 2020) rely on manually annotated data, which introduces human bias and is time-consuming to obtain.
Variational autoencoders (Fei et al., 2021) overlooked latent aspects, whereas this work focuses explicitly on detecting latent aspects.
Contribution of This Paper:
This paper proposes an unsupervised method that models aspects as latent variables using Latent Dirichlet Allocation (LDA) to detect latent aspects from customer reviews. The method improves upon existing techniques by effectively identifying latent aspects without human supervision.
Proposed Methods:
The authors propose using Latent Dirichlet Allocation (LDA) to model aspects as latent variables. They assume that when customers write a review, they choose an aspect and then select words related to that aspect to express their opinion. LDA is used to infer the distribution of aspects and the probability of words associated with each aspect.
Experiments:
Datasets: The model is trained on Google restaurant reviews and evaluated on the SemEval 2014, 2015, and 2016 datasets. These datasets consist of annotated reviews with explicit aspect labels.
Evaluation: The model is evaluated based on its ability to predict latent aspects in noisy, unsolicited reviews by comparing predicted aspects to ground-truth explicit aspects from SemEval datasets.
Gaps in This Work:
The model's evaluation is limited to one domain (restaurant reviews), and its performance should be tested across multiple domains to assess its generalizability.
Paper Title: Latent Aspect Detection from Online Unsolicited Customer Reviews
Introduction: Aspect Detection helps businesses understand customer opinions and address potential issues, thereby preventing customer churn. Identifying and analyzing aspects in customer reviews allows businesses to prioritize improvements and make informed decisions to retain customers.
Main Problem: Current methods for aspect detection often rely on supervised learning, which involves training on manually labeled data. However, this approach becomes impractical when dealing with online reviews that are typically shorter, more informal, and where aspects are often implicitly expressed rather than explicitly mentioned. This paper presents an unsupervised method to extract latent aspects that are common in online reviews but not overtly stated.
Illustrative Example: Implicit aspect: "We were given a table far from the river." [Aspect: Management] Explicit aspect: "The menu is extensive, and there’s a bar with live music." [Aspect: Bar/Menu]
Input: A review from an online platform (e.g., restaurant reviews).
Output: Detected latent aspects present in the review without explicit surface forms.
Motivation: Previous works in this area have not effectively addressed the challenge of detecting latent aspects. Existing unsupervised methods have mainly focused on explicit aspects and neglected latent ones, making it necessary to develop an approach specifically targeting latent aspects.
Related Works and Their Gaps: Rule-based methods (Hai et al., 2011; Zeng and Li, 2013) use association rule mining to detect hidden aspects but are limited in scalability across various domains. Supervised methods (Peng et al., 2020; Zhang et al., 2020) rely on manually annotated data, which introduces human bias and is time-consuming to obtain. Variational autoencoders (Fei et al., 2021) overlooked latent aspects, whereas this work focuses explicitly on detecting latent aspects.
Contribution of This Paper: This paper proposes an unsupervised method that models aspects as latent variables using Latent Dirichlet Allocation (LDA) to detect latent aspects from customer reviews. The method improves upon existing techniques by effectively identifying latent aspects without human supervision.
Proposed Methods: The authors propose using Latent Dirichlet Allocation (LDA) to model aspects as latent variables. They assume that when customers write a review, they choose an aspect and then select words related to that aspect to express their opinion. LDA is used to infer the distribution of aspects and the probability of words associated with each aspect.
Experiments: Datasets: The model is trained on Google restaurant reviews and evaluated on the SemEval 2014, 2015, and 2016 datasets. These datasets consist of annotated reviews with explicit aspect labels. Evaluation: The model is evaluated based on its ability to predict latent aspects in noisy, unsolicited reviews by comparing predicted aspects to ground-truth explicit aspects from SemEval datasets.
Implementation: https://github.com/MohammadForouhesh/latent-aspect-detection
Gaps in This Work: The model's evaluation is limited to one domain (restaurant reviews), and its performance should be tested across multiple domains to assess its generalizability.