Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network

Lee, Gichang; Jeong, Jechang; Seo, Seungwan; Kim, Czang Yeob; Kang, Pilsung

doi:10.1016/j.knosys.2018.04.006

Cited by 101 publications

(41 citation statements)

References 36 publications

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“…Lee et al applied class activation mapping to the task of sentiment classification [26]. They trained multi-channel CNNs on movie reviews, and then demonstrated sentiment-specific expressions mined by CAMs.…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Analysis Using Aspect Map

Noh

Park

2019

Applied Sciences

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Aspect-based sentiment analysis (ABSA) is the task of classifying the sentiment of a specific aspect in a text. Because a single text usually has multiple aspects which are expressed independently, ABSA is a crucial task for in-depth opinion mining. A key point of solving ABSA is to align sentiment expressions with their proper target aspect in a text. Thus, many recent neural models have applied attention mechanisms to learning the alignment. However, it is problematic to depend solely on attention mechanisms to achieve this, because most sentiment expressions such as "nice" and "bad" are too general to be aligned with a proper aspect even through an attention mechanism. To solve this problem, this paper proposes a novel convolutional neural network (CNN)-based aspect-level sentiment classification model, which consists of two CNNs. Because sentiment expressions relevant to an aspect usually appear near the aspect expressions of the aspect, the proposed model first finds the aspect expressions for a given aspect and then focuses on the sentiment expressions around the aspect expressions to determine the final sentiment of an aspect. Thus, the first CNN extracts the positional information of aspect expressions for a target aspect and expresses the information as an aspect map. Even if there exist no data with annotations on direct relation between aspects and their expressions, the aspect map can be obtained effectively by learning it in a weakly supervised manner. Then, the second CNN classifies the sentiment of the target aspect in a text using the aspect map. The proposed model is evaluated on SemEval 2016 Task 5 dataset and is compared with several baseline models. According to the experimental results, the proposed model does not only outperform the baseline models but also shows state-of-the-art performance for the dataset.

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Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Analysis Using Aspect Map

Noh

Park

2019

Applied Sciences

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“…Word to Vector representation is the word vector model [37][38][39][40][41], which defines the words in the word space vector representation. This is the simple representation as those used in the text categorization.…”

Section: A Word To Vector Representationmentioning

confidence: 99%

AEAO: Auto Encoder with Adam Optimizer Method for Efficient Document Indexing of Big Data

Bhargavi*¹,

Murthy²,

Rao³

2019

IJRTE

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In the big data era, the document classification became an active research area due to the explosive nature in the volumes of data. Document Indexing is one of the important tasks under text classification. The objective of this research is to increase the performance of the document indexing by proposing Adam optimizer in the auto-encoder. Due to the larger dimension and multi-class classification problem, the accuracy of document indexing is reduced. In this paper, an enhanced auto encoder is used based on the objective function of the Adam optimization (AEAO), which improves the learning rate and accuracy of indexing. The documents from the 20-newsgroup data set are converted into vector representation, and then the cosine similarity and Pearson correlation have been measured from the vector. The word to vector representation has words in the vector form and the frequency of words in the document increases their value. The Adam optimization technique selects the features by using similarity values and improves the learning rate. The auto encoder classifier classifies the document based on the objective function of the Adam optimizer. The experiment is conducted using python and the result infers that the classification performance of AEAO is better than that of Similarity-based classification framework for Multiple-Instance Learning and Self-Adaptive LSH encoding for multi-instance Learning techniques in terms of parameters like precision, recall and fscore

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“…This vector put unique index for each word except all zeros [51]. In literature, different word embedding techniques namely random vectors, Word2Vec, Glove (Global vectors for word representation), FastText [48] and Character vector [46] are experimented to construct input matrix for CNN classifier. Word2Vec is a predictive model [51].…”

Section: A Word Embedding Technique-word2vecmentioning

confidence: 99%

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Adamuthe¹,

Jagtap²

2019

IJISA

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This paper presents an investigation of the convolutional neural network (CNN) with Word2Vec word embedding technique for text classification. Performance of CNN is tested on seven benchmark datasets with a different number of classes, training and testing samples. Test classification results obtained from proposed CNN are compared with results of CNN models and other classifiers reported in the literature. Investigation shows that CNN models are better suitable for text classification than other techniques. The main objective of the paper is to identify best-fitted parameter values batch size, epochs, activation function, dropout rates and feature maps values. Results of proposed CNN are better than many other classification techniques reported in the literature for Yelp Review Polarity dataset and Amazon Review Polarity dataset. For all the seven datasets, accuracy obtained by proposed CNN is close to the best-known results from the literature.

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Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network

Cited by 101 publications

References 36 publications

Aspect-Based Sentiment Analysis Using Aspect Map

Aspect-Based Sentiment Analysis Using Aspect Map

AEAO: Auto Encoder with Adam Optimizer Method for Efficient Document Indexing of Big Data

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

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