scholarly journals Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 473
Author(s):  
Yongpeng Wang ◽  
Hong Yu ◽  
Guoyin Wang ◽  
Yongfang Xie
Keyword(s):  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Mapping Function ◽  
Linear Mapping ◽  
Feature Mapping ◽  
Target Domain ◽  
User Reviews ◽  
Cross Domain ◽  
Semantic Orientation ◽  
Label Information

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

Fusion of part-of-speech vectors and attention mechanisms for cross-domain sentiment analysis

2021 ◽  
pp. 1-9
Author(s):  
Ting Lu ◽  
Yan Xiang ◽  
Junge Liang ◽  
Li Zhang ◽  
Mingfang Zhang
Keyword(s):  
Sentiment Analysis ◽  
Poor Performance ◽  
Feature Representation ◽  
Grand Challenge ◽  
Feature Mapping ◽  
Target Domain ◽  
Specific Domain ◽  
Part Of Speech ◽  
Cross Domain ◽  
Speech Information

The grand challenge of cross-domain sentiment analysis is that classifiers trained in a specific domain are very sensitive to the discrepancy between domains. A sentiment classifier trained in the source domain usually have a poor performance in the target domain. One of the main strategies to solve this problem is the pivot-based strategy, which regards the feature representation as an important component. However, part-of-speech information was not considered to guide the learning of feature representation and feature mapping in previous pivot-based models. Therefore, we present a fused part-of-speech vectors and attention-based model (FAM). In our model, we fuse part-of-speech vectors and feature word embeddings as the representation of features, giving deep semantics to mapping features. And we adopt Multi-Head attention mechanism to train the cross-domain sentiment classifier to obtain the connection between different features. The results of 12 groups comparative experiments on the Amazon dataset demonstrate that our model outperforms all baseline models in this paper.

Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 29-45
Author(s):  
Kwun-Ping Lai ◽  
Jackie Chun-Sing Ho ◽  
Wai Lam
Keyword(s):  
Knowledge Transfer ◽  
Sentiment Analysis ◽  
Real World ◽  
Coarse Grained ◽  
Target Domain ◽  
Training Strategy ◽  
Fine Grained ◽  
Cross Domain ◽  
Proposed Model ◽  
Novel Model

The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

Self-Supervised Pre-training on the Target Domain for Cross-Domain Person Re-identification

2021 ◽  
Author(s):  
Junyin Zhang ◽  
Yongxin Ge ◽  
Xinqian Gu ◽  
Boyu Hua ◽  
Tao Xiang
Keyword(s):  
Target Domain ◽  
Cross Domain

Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

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