Direct Negative Opinions in Online Discussions

Musat, Claudiu-Cristian; Faltings, Boi; Rousille, Philippe

doi:10.1109/socialcom.2013.28

Cited by 4 publications

(2 citation statements)

References 15 publications

(11 reference statements)

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“…research that extracts this element from user reviews and incorporates it into CF [7,9,27,41]. Musto et al [6] extracted aspects and sub-aspects using Kullback-Leibler divergence, a non-symmetric measure and Nielsen's lexicon [42] based on the AFINN wordlist to assign the sentiment score for each extracted aspect/sub-aspect.…”

Section: B Aspect-based Collaborative Filteringmentioning

confidence: 99%

To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering

et al. 2023

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Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users' behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect th e performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google's pre-trained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the kNN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.

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Section: B Aspect-based Collaborative Filteringmentioning

confidence: 99%

To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering

et al. 2023

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“…Textual reviews have more detailed user opinions and item attributes than ratings [7], and methods based on reviews and ratings effectively solve the data sparsity and cold-start problems. These methods use different strategies to extract features from reviews, and early research attempted to model topics from reviews, obtain user preferences, and then use collaborative filtering for recommendations [8][9][10]. Some other researchers have used clustering to process review texts to categorize users and items from reviews and thus improve the recommendation performance based on category similarity [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

ASKAT: Aspect Sentiment Knowledge Graph Attention Network for Recommendation

Cui,

Zhou,

et al. 2024

Electronics

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In modern online life, recommender systems can help us filter unimportant information. Researchers of recommendation algorithms usually utilize historical interaction data to mine potential user preferences. However, most existing methods use rating data to mine user interest preferences, ignoring rich textual information such as reviews. Although some researchers have attempted to combine ratings and reviews for recommendation, we believe the following shortcomings still exist. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools. Second, existing methods do not fully utilize the features extracted from reviews. Further, existing methods focus only on the aspects that users like, while ignoring the aspects that users dislike, and they cannot completely model users’ true preferences. To address the above issues, in this paper, we propose a recommendation model based on the aspect of the sentiment knowledge graph attention network (ASKAT). We first use the improved aspect-based sentiment analysis algorithm to extract aspectual sentiment features from reviews. Then, to overcome the difficulty in underutilizing the information extracted from the comments, we build aspects of sentiment-enhanced collaborative knowledge mapping. After that, we propose a new graph attention network that uses sentiment-aware attention mechanisms to aggregate neighbour information. Finally, our experimental results on three datasets, Movie, Amazon book, and Yelp, show that our model consistently outperforms the baseline model in two recommendation scenarios, click-through-rate prediction and Top-k recommendation. Compared with other models, the method shows significant improvement in both recommendation accuracy and personalised recommendation effectiveness.

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