scholarly journals Interactive Attention Networks for Aspect-Level Sentiment Classification

2017 ◽  
Author(s):  
Dehong Ma ◽  
Sujian Li ◽  
Xiaodong Zhang ◽  
Houfeng Wang
Keyword(s):  
Interactive Learning ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Special Treatment ◽  
Attention Networks ◽  
Specific Target

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling thier contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.

scholarly journals Multiple Interactive Attention Networks for Aspect-Based Sentiment Classification

2020 ◽  
Vol 10 (6) ◽  
pp. 2052
Author(s):  
Dianyuan Zhang ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Hongli Pei ◽  
Wenqing Wu ◽  
...  
Keyword(s):  
Target Word ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Context Word ◽  
Attention Network ◽  
Attention Networks ◽  
The Core

Aspect-Based (also known as aspect-level) Sentiment Classification (ABSC) aims at determining the sentimental tendency of a particular target in a sentence. With the successful application of the attention network in multiple fields, attention-based ABSC has aroused great interest. However, most of the previous methods are difficult to parallelize, insufficiently obtain, and fuse the interactive information. In this paper, we proposed a Multiple Interactive Attention Network (MIN). First, we used the Bidirectional Encoder Representations from Transformers (BERT) model to pre-process the data. Then, we used the partial transformer to obtain a hidden state in parallel. Finally, we took the target word and the context word as the core to obtain and fuse the interactive information. Experimental results on the different datasets showed that our model was much more effective.

scholarly journals Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks

2018 ◽  
Author(s):  
Jingjing Wang ◽  
Jie Li ◽  
Shoushan Li ◽  
Yangyang Kang ◽  
Min Zhang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Hierarchical Network ◽  
Attention Networks ◽  
Word Level ◽  
Sentence Level

Aspect sentiment classification, a challenging task in sentiment analysis, has been attracting more and more attention in recent years. In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification. Specifically, we first adopt sentence-level discourse segmentation to segment a sentence into several clauses. Then, we leverage multiple Bi-directional LSTM layers to encode all clauses and propose a word-level attention layer to capture the importance degrees of words in each clause. Third and finally, we leverage another Bi-directional LSTM layer to encode the outputs from the former layers and propose a clause-level attention layer to capture the importance degrees of all the clauses inside a sentence. Experimental results on the laptop and restaurant datasets from SemEval-2015 demonstrate the effectiveness of our proposed approach to aspect sentiment classification.

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

scholarly journals A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification

2018 ◽  
Author(s):  
Shuming Ma ◽  
Xu Sun ◽  
Junyang Lin ◽  
Xuancheng Ren
Keyword(s):  
Hierarchical Structure ◽  
Online Reviews ◽  
Text Summarization ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Joint Learning ◽  
End To End ◽  
Abstractive Summarization ◽  
Main Ideas ◽  
Different Levels

Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which ``summarizes'' the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical end-to-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further ``summarization'' of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment classification.

An Efficient Framework for Vietnamese Sentiment Classification

2020 ◽  
Author(s):  
Cuong V. Nguyen ◽  
Khiem H. Le ◽  
Anh M. Tran ◽  
Binh T. Nguyen
Keyword(s):  
Product Quality ◽  
Sentiment Analysis ◽  
New Products ◽  
Classification Problem ◽  
Research Community ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Data Sets ◽  
Online Retailers

With the booming development of E-commerce platforms in many counties, there is a massive amount of customers’ review data in different products and services. Understanding customers’ feedbacks in both current and new products can give online retailers the possibility to improve the product quality, meet customers’ expectations, and increase the corresponding revenue. In this paper, we investigate the Vietnamese sentiment classification problem on two datasets containing Vietnamese customers’ reviews. We propose eight different approaches, including Bi-LSTM, Bi-LSTM + Attention, Bi-GRU, Bi-GRU + Attention, Recurrent CNN, Residual CNN, Transformer, and PhoBERT, and conduct all experiments on two datasets, AIVIVN 2019 and our dataset self-collected from multiple Vietnamese e-commerce websites. The experimental results show that all our proposed methods outperform the winning solution of the competition “AIVIVN 2019 Sentiment Champion” with a significant margin. Especially, Recurrent CNN has the best performance in comparison with other algorithms in terms of both AUC (98.48%) and F1-score (93.42%) in this competition dataset and also surpasses other techniques in our dataset collected. Finally, we aim to publish our codes, and these two data-sets later to contribute to the current research community related to the field of sentiment analysis.

scholarly journals Interactive Multi-Head Attention Networks for Aspect-Level Sentiment Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 160017-160028 ◽  
Author(s):  
Qiuyue Zhang ◽  
Ran Lu ◽  
Qicai Wang ◽  
Zhenfang Zhu ◽  
Peiyu Liu
Keyword(s):  
Sentiment Classification ◽  
Attention Networks

Visual-textual sentiment classification with bi-directional multi-level attention networks

2019 ◽  
Vol 178 ◽  
pp. 61-73 ◽  
Author(s):  
Jie Xu ◽  
Feiran Huang ◽  
Xiaoming Zhang ◽  
Senzhang Wang ◽  
Chaozhuo Li ◽  
...  
Keyword(s):  
Sentiment Classification ◽  
Attention Networks ◽  
Multi Level

scholarly journals On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Asriyanti Indah Pratiwi ◽  
Adiwijaya
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Classification Scheme ◽  
Information Gain ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Enormous Number

Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.

scholarly journals Extracting Customer Reviews from Online Shopping and Its Perspective on Product Design

2019 ◽  
Vol 06 (01) ◽  
pp. 43-56
Author(s):  
Kieu Que Anh ◽  
Yukari Nagai ◽  
Le Minh Nguyen
Keyword(s):  
Product Design ◽  
Early Phase ◽  
Online Shopping ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Customer Reviews ◽  
Proposed Model

This paper presents a study on how we can extract helpful review from customers and its effect on the early phase when designing a product. We present a framework for analyzing the reviews from online shopping sites by detecting helpful reviews, aspects, and top reviews. We conduct a through analysis on review comments of the Amazon site and form a novel framework which can automatically extract useful information from review documents and it can also collaboratively work between designers and opinion customers. Experimental results on helpful review identification and sentiment classification showed that the proposed model achieved promising results. We also conduct an interview with designers to assert whether or not the proposed framework is effective. The results showed that the proposed framework is helpful for designers.

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