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

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 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 Extraction of Relying Factors to be Diabetic in Pregnant Women using Attribute Mutual Information

2019 ◽  
Vol 9 (2) ◽  
pp. 4959-4961
Keyword(s):  
Health Care ◽  
Mutual Information ◽  
Pregnant Women ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Daily Basis ◽  
Medical Test ◽  
Data Set ◽  
Word Level ◽  
Sentence Level

Since a decade research over sentiment analysis and opinion mining was evolving slowing and emerging widely with greater perspectives and objectives. Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated on a daily basis. This analysis relies on the opinions made by the individuals. These opinions are text, may be positive or negative or a phrase which gives significance to the context. Also these opinions have the power of expressing the context besides drags the attention of new folks. Expressing such opinions ranges from documents level, to the sentence level, to phrase level, to word level and to special symbol level. All these opinion types are labelled with common name Sentiment Analysis. Sentiment Analysis is health care is evolving narrowly with wider research strings. This paper mainly focuses in identifying Sentiments in health care. These sentiments can be medical test values which may be numeric and nominal; sometimes in text too. Such sentiments are identified with pre-fragmentation of data set and Pointwise Mutual Information measure. To accomplish this data of hypertensive pregnant women is considered.

scholarly journals Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 9725-9732
Author(s):  
Xiaorui Zhou ◽  
Senlin Luo ◽  
Yunfang Wu
Keyword(s):  
Reading Comprehension ◽  
Semantic Similarity ◽  
State Of The Art ◽  
Deep Understanding ◽  
Hierarchical Architecture ◽  
Hierarchical Network ◽  
Word Level ◽  
Human Evaluation ◽  
Sentence Level ◽  
The Relationship

In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential in generating creative text, the previous neural methods for distractor generation ignore two important aspects. First, they didn't model the interactions between the article and question, making the generated distractors tend to be too general or not relevant to question context. Second, they didn't emphasize the relationship between the distractor and article, making the generated distractors not semantically relevant to the article and thus fail to form a set of meaningful options. To solve the first problem, we propose a co-attention enhanced hierarchical architecture to better capture the interactions between the article and question, thus guide the decoder to generate more coherent distractors. To alleviate the second problem, we add an additional semantic similarity loss to push the generated distractors more relevant to the article. Experimental results show that our model outperforms several strong baselines on automatic metrics, achieving state-of-the-art performance. Further human evaluation indicates that our generated distractors are more coherent and more educative compared with those distractors generated by baselines.

scholarly journals A Statistical Parsing Framework for Sentiment Classification

2015 ◽  
Vol 41 (2) ◽  
pp. 293-336 ◽  
Author(s):  
Li Dong ◽  
Furu Wei ◽  
Shujie Liu ◽  
Ming Zhou ◽  
Ke Xu
Keyword(s):  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
Data Sets ◽  
Syntactic Parsing ◽  
Benchmark Data ◽  
Sentence Level ◽  
Statistical Parsing ◽  
Parse Trees ◽  
Context Free

We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that use syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same way as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description of the sentiment parsing framework. We develop the parsing model to obtain possible sentiment parse trees for a sentence, from which the polarity model is proposed to derive the sentiment strength and polarity, and the ranking model is dedicated to selecting the best sentiment tree. We train the parser directly from examples of sentences annotated only with sentiment polarity labels but without any syntactic annotations or polarity annotations of constituents within sentences. Therefore we can obtain training data easily. In particular, we train a sentiment parser, s.parser, from a large amount of review sentences with users' ratings as rough sentiment polarity labels. Extensive experiments on existing benchmark data sets show significant improvements over baseline sentiment classification approaches.

scholarly journals Fusing Multi-Modal Char-Level Embeddings for Chinese Sentence Sentiment Analysis

2021 ◽  
Author(s):  
Dong Liu ◽  
Caihuan Zhang ◽  
Yongxin Zhang ◽  
Youzhong Ma
Keyword(s):  
Sentiment Analysis ◽  
Chinese Character ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Visual Features ◽  
Chinese Characters ◽  
Writing Systems ◽  
Phonetic Information ◽  
Context Features

Abstract Chinese characters are one of the logographic writing systems. There is some association between semantics and structures, shape, phonetic information of Chinese characters. In this work, multi-modal Chinese character-level embeddings are extracted, including visual features, pre-trained embeddings, shapes, and phonetic information. These embedding sequences of Chinese sentences are first fed into individual Bi-LSTM networks to capture context features, and then fused into one vector for sentiment analysis. Experimental results validate that multi-modal character-level can contribute to Chinese sentence sentiment classification. And its effect on the result is analyzed by modal features ablation test.

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.

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