Exploiting Rich Textual User-Product Context for Improving Sentiment
Analysis
- URL: http://arxiv.org/abs/2212.08888v1
- Date: Sat, 17 Dec 2022 14:57:52 GMT
- Title: Exploiting Rich Textual User-Product Context for Improving Sentiment
Analysis
- Authors: Chenyang Lyu, Linyi Yang, Yue Zhang, Yvette Graham, Jennifer Foster
- Abstract summary: We propose a method to explicitly employ historical reviews belonging to the same user/product to initialize representations.
Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks show that our approach substantially outperforms previous state-of-the-art.
- Score: 21.840121866597563
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: User and product information associated with a review is useful for sentiment
polarity prediction. Typical approaches incorporating such information focus on
modeling users and products as implicitly learned representation vectors. Most
do not exploit the potential of historical reviews, or those that currently do
require unnecessary modifications to model architecture or do not make full use
of user/product associations. The contribution of this work is twofold: i) a
method to explicitly employ historical reviews belonging to the same
user/product to initialize representations, and ii) efficient incorporation of
textual associations between users and products via a user-product
cross-context module. Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks
show that our approach substantially outperforms previous state-of-the-art.
Since we employ BERT-base as the encoder, we additionally provide experiments
in which our approach performs well with Span-BERT and Longformer. Furthermore,
experiments where the reviews of each user/product in the training data are
downsampled demonstrate the effectiveness of our approach under a low-resource
setting.
Related papers
- Centrality-aware Product Retrieval and Ranking [14.710718676076327]
This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries.
We curate samples from eBay, manually annotated with buyer-centric relevance scores and centrality scores, which reflect how well the product title matches the users' intent.
We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimises for the user intent in semantic product search.
arXiv Detail & Related papers (2024-10-21T11:59:14Z) - Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation [26.214148426964794]
We introduce new datasets and evaluation methods that focus on the users' sentiments.
We construct the datasets by explicitly extracting users' positive and negative opinions from their post-purchase reviews.
We propose to evaluate systems based on whether the generated explanations align well with the users' sentiments.
arXiv Detail & Related papers (2024-10-17T06:15:00Z) - Modeling Entities as Semantic Points for Visual Information Extraction
in the Wild [55.91783742370978]
We propose an alternative approach to precisely and robustly extract key information from document images.
We explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities.
The proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models.
arXiv Detail & Related papers (2023-03-23T08:21:16Z) - Towards Personalized Review Summarization by Modeling Historical Reviews
from Customer and Product Separately [59.61932899841944]
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
We propose the Heterogeneous Historical Review aware Review Summarization Model (HHRRS)
We employ a multi-task framework that conducts the review sentiment classification and summarization jointly.
arXiv Detail & Related papers (2023-01-27T12:32:55Z) - Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform.
Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online.
Intent modeling is thus critical for understanding users and optimizing long-term user experience.
arXiv Detail & Related papers (2022-11-17T19:00:24Z) - Incorporating Relevance Feedback for Information-Seeking Retrieval using
Few-Shot Document Re-Ranking [56.80065604034095]
We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant.
To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
arXiv Detail & Related papers (2022-10-19T16:19:37Z) - Personalizing Intervened Network for Long-tailed Sequential User
Behavior Modeling [66.02953670238647]
Tail users suffer from significantly lower-quality recommendation than the head users after joint training.
A model trained on tail users separately still achieve inferior results due to limited data.
We propose a novel approach that significantly improves the recommendation performance of the tail users.
arXiv Detail & Related papers (2022-08-19T02:50:19Z) - Improving Document-Level Sentiment Analysis with User and Product
Context [16.47527363427252]
We investigate incorporating additional review text available at the time of sentiment prediction.
We achieve this by explicitly storing representations of reviews written by the same user and about the same product.
Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.
arXiv Detail & Related papers (2020-11-18T10:59:14Z) - E-commerce Query-based Generation based on User Review [1.484852576248587]
We propose a novel seq2seq based text generation model to generate answers to user's question based on reviews posted by previous users.
Given a user question and/or target sentiment polarity, we extract aspects of interest and generate an answer that summarizes previous relevant user reviews.
arXiv Detail & Related papers (2020-11-11T04:58:31Z) - Disentangled Graph Collaborative Filtering [100.26835145396782]
Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data.
By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations.
DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
arXiv Detail & Related papers (2020-07-03T15:37:25Z) - Exploration-Exploitation Motivated Variational Auto-Encoder for
Recommender Systems [1.52292571922932]
We introduce an exploitation-exploration motivated variational auto-encoder (XploVAE) to collaborative filtering.
To facilitate personalized recommendations, we construct user-specific subgraphs, which contain the first-order proximity capturing observed user-item interactions.
A hierarchical latent space model is utilized to learn the personalized item embedding for a given user, along with the population distribution of all user subgraphs.
arXiv Detail & Related papers (2020-06-05T17:37:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.